hypothesis in education

Learning Theories

There are five basic types of learning theory: behaviorist, cognitive, constructivist, social, and experiential. This section provides a brief introduction to each type of learning theory.

Behaviorism In Psychology

Albert Bandura's Social Learning Theory

Reviewed by Olivia Guy-Evans, MSc

Aversion Therapy & Examples of Aversive Conditioning

Bloom’s taxonomy of learning.

Reviewed by Saul Mcleod, PhD

Jean Piaget

Behaviorism, neuroscience.

Jean Piaget's theory of cognitive development suggests that children move through four different stages of intellectual development which reflect the increasing sophistication of children's thoughts. Child development is determined by biological maturation and interaction with the environment.

Learn More: Piaget's Stages of Cognitive Development

Behaviorism is a theory of learning that states all behaviors are learned through interaction with the environment through a process called conditioning. Thus, behavior is simply a response to environmental stimuli.

Learn More: Behaviorist Approach in Psychology

Sigmund Freud (1856 to 1939) was the founding father of psychoanalysis, a method for treating mental illness and a theory that explains human behavior. His theories are clinically derived, based on what his patients told him during therapy.

Learn More: Sigmund Freud's Influence on Psychology

An approach is a perspective that involves certain assumptions about human behavior: the way people function, which aspects of them are worthy of study, and what research methods are appropriate for undertaking this study. The five major psychological perspectives are biological, psychodynamic, behaviorist, cognitive, and humanistic.

Learn More: Major Perspectives in Modern Psychology

Neuroscience is the branch of science concerned with studying the nervous system. It is a multidisciplinary field integrating numerous perspectives from biology, psychology, and medicine. It consists of several sub-fields ranging from the study of neurochemicals to the study of behavior and thought.

Learn More: What is Neuroscience?

Frequent Asked Questions

Is psychodynamic same as psychoanalytic?

The words psychodynamic and psychoanalytic are often confused. Remember that Freud’s theories were psychoanalytic, whereas the term ‘psychodynamic’ refers to both his theories and those of his followers, such as Carl Jung, Anna Freud, and Erik Erikson.

Learn More: Psychodynamic Approach

What is developmental psychology?

Developmental psychology is a scientific approach which aims to explain how thinking, feeling, and behavior change throughout a person’s life. A significant proportion of theories within this discipline focus upon development during childhood, as this is the period during an individual’s lifespan when the most change occurs.

Learn More: Developmental Psychology

What is Freud’s psychosexual theory?

Sigmund Freud proposed that personality development in childhood takes place during five psychosexual stages, which are the oral, anal, phallic, latency, and genital stages.

During each stage, sexual energy (libido) is expressed in different ways and through different body parts.

Learn More: Freud’s Psychosexual Stages of Development

What Is object permanence in Piaget’s theory?

Object permanence means knowing that an object still exists, even if it is hidden. It requires the ability to form a mental representation (i.e. a schema) of the object.

The attainment of object permanence generally signals the transition from the sensorimotor stage to the  preoperational stage of development .

Learn More: What Is Object Permanence According To Piaget?

What is the difference between a psychology and sociology?

Psychology studies the mind of an individual to understand human behavior and social and emotional reactions, whereas sociology looks beyond individuals and examines societal institutions and groups of people.

Learn More: Similarities and Differences Between Sociology and Psychology

Explore Learning Theories

Bandura's Bobo Doll Experiment on Social Learning

Jerome Bruner's Theory Of Learning And Cognitive Development

Classical Conditioning: How It Works With Examples

Constructivism Learning Theory & Philosophy of Education

Conditioned Stimulus In Classical Conditioning

Gagne's Conditions of Learning Theory

Drive-Reduction Theory of Motivation In Psychology

Edward Thorndike: The Law of Effect

Expository Teaching: Ausubel Theory of Learning

Fluid Intelligence vs. Crystallized Intelligence

Higher Order Conditioning In Psychology

What Is Intelligence In Psychology

Information Processing Theory In Psychology

John Dewey on Education: Impact & Theory

Kolb's Learning Styles and Experiential Learning Cycle

Montessori Theory of Education

Musical Intelligence: Definition, Examples & Characteristics

What Is Negative Reinforcement?

Howard Gardner's Theory of Multiple Intelligences

Operant Conditioning: What It Is, How It Works, and Examples

Pavlov’s Dogs Experiment and Pavlovian Conditioning Response

Positive Reinforcement: What Is It and How Does It Work?

the Premack Principle in Psychology: Definition and Examples

Piaget's Theory and Stages of Cognitive Development

Schedules of Reinforcement in Psychology (Examples)

Albert Bandura's Social Cognitive Theory

Systematic Desensitization Therapy In Psychology

Latent Learning In Psychology and How It Works

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Theories of Child Development and Their Impact on Early Childhood Education and Care

• Published: 29 October 2021
• Volume 51 , pages 15–30, ( 2023 )

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Developmental theorists use their research to generate philosophies on children’s development. They organize and interpret data based on a scheme to develop their theory. A theory refers to a systematic statement of principles related to observed phenomena and their relationship to each other. A theory of child development looks at the children's growth and behavior and interprets it. It suggests elements in the child's genetic makeup and the environmental conditions that influence development and behavior and how these elements are related. Many developmental theories offer insights about how the performance of individuals is stimulated, sustained, directed, and encouraged. Psychologists have established several developmental theories. Many different competing theories exist, some dealing with only limited domains of development, and are continuously revised. This article describes the developmental theories and their founders who have had the greatest influence on the fields of child development, early childhood education, and care. The following sections discuss some influences on the individuals’ development, such as theories, theorists, theoretical conceptions, and specific principles. It focuses on five theories that have had the most impact: maturationist, constructivist, behavioral, psychoanalytic, and ecological. Each theory offers interpretations on the meaning of children's development and behavior. Although the theories are clustered collectively into schools of thought, they differ within each school.

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Although Watson was the first to maintain explicitly that psychology was a natural science, behaviorism in both theory and practice had originated much earlier than 1913. Watson offered a vital incentive to behaviorism, but several others had started the process. He never stated to have created “behavioral psychology.” Some behaviorists consider him a model of the approach rather than an originator of behaviorism (Malone, 2014 ). Still, his presence has significantly influenced the status of present psychology and its development.

Alschuler, R., & Hattwick, L. (1947). Painting and personality . University of Chicago Press.

Google Scholar  

Axline, V. (1974). Play therapy . Ballentine Books.

Berk, L. (2021). Infants, children, and adolescents . Pearson.

Bijou, S. W. (1975). Development in the preschool years: A functional analysis. American Psychologist, 30 (8), 829–837. https://doi.org/10.1037/h0077069

Article   Google Scholar  

Bijou, S. W. (1977). Behavior analysis applied to early childhood education. In B. Spodek & H. J. Walberg (Eds.), Early childhood education: Issues and insights (pp. 138–156). McCutchan Publishing Corporation.

Boghossion, P. (2006). Behaviorism, constructivism, and Socratic pedagogy. Educational Philosophy and Theory, 38 (6), 713–722. https://doi.org/10.1111/j.1469-5812.2006.00226.x

Bower, B. (1986). Skinner boxing. Science News, 129 (6), 92–94. https://doi.org/10.2307/3970364

Briner, M. (1999). Learning theories . University of Colorado.

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45 (1), 1–5. https://doi.org/10.2307/1127743

Bronfenbrenner, U. (1979). The ecology of human development . Harvard University Press.

Bruner, J. S. (1960). The process of education . Harvard University Press.

Bruner, J. S. (1990). Acts of meaning . Harvard University Press.

Bruner, J. (2004). A short history of psychological theories of learning. Daedalus, 133 (1), 13–20. https://doi.org/10.1162/001152604772746657

Coles, R., Hunt, R., & Maher, B. (2002). Erik Erikson: Faculty of Arts and Sciences Memorial Minute. Harvard Gazette Archives . http://www.hno.harvard.edu/gazette/2002/03.07/22-memorialminute.html

Editors of Encyclopaedia Britannica. (2020). Erik Erikson . https://www.britannica.com/biography/Erik-Erikson

Erikson, E. H. (1950). Childhood and society . Norton.

Freud, A. (1935). Psychoanalysis for teachers and parents . Emerson Books.

Friedman, L. J. (1999). Identity’s architect: A biography of Erik H . Scribner Publishing Company.

Gesell, A. (1928). In infancy and human growth . Macmillan Co.

Book   Google Scholar  

Gesell, A. (1933). Maturation and the patterning of behavior. In C. Murchison (Ed.), A handbook of child psychology (pp. 209–235). Russell & Russell/Atheneum Publishers. https://doi.org/10.1037/11552-004

Chapter   Google Scholar  

Gesell, A., & Ilg, F. L. (1946). The child from five to ten . Harper & Row.

Gesell, A., Ilg, F. L., & Ames, L. B. (1978). Child behavior . Harper & Row.

Gesell, A., & Thompson, H. (1938). The psychology of early growth, including norms of infant behavior and a method of genetic analysis . Macmillan Co.

von Glasersfeld, E. (1995). Radical constructivism: A way of knowing and learning . Falmer.

von Glasersfeld, E. (2005). Introduction: Aspects of constructivism. In C. T. Fosnot (Ed.), Constructivism: Theory, perspectives and practice (pp. 3–7). Teachers College.

Graham, S., & Weiner, B. (1996). Theories and principles of motivation. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 63–84). Macmillan Library Reference.

Gray, P. O., & Bjorklund, D. F. (2017). Psychology (8th ed.). Worth Publishers.

Hilgard, E. R. (1987). Psychology in America: A historical survey . Harcourt Brace Jovanovich.

Hunt, J. . Mc. V. (1961). Intelligence and experience . Ronald Press.

Jenkins, E. W. (2000). Constructivism in school science education: Powerful model or the most dangerous intellectual tendency? Science and Education, 9 , 599–610. https://doi.org/10.1023/A:1008778120803

Jones, M. G., & Brader-Araje, L. (2002). The impact of constructivism on education: Language, discourse, and meaning. American Communication Studies, 5 (3), 1–1.

Kamii, C., & DeVries, R. (1978/1993.) Physical knowledge in preschool education: Implications of Piaget’s theory . Teachers College Press.

King, P. H. (1983). The life and work of Melanie Klein in the British Psycho-Analytical Society. The International Journal of Psycho-Analysis, 64 (Pt 3), 251–260. PMID: 6352537.

Malone, J. C. (2014). Did John B. Watson really “Found” Behaviorism? The Behavior Analyst , 37 (1) ,  1–12. https://doi-org.proxy-um.researchport.umd.edu/10.1007/s40614-014-0004-3

Miller, P. H. (2016). Theories of developmental psychology (6th ed.). Worth Publishers.

Morphett, M. V., & Washburne, C. (1931). When should children begin to read? Elementary School Journal, 31 (7), 496–503. https://doi.org/10.1086/456609

Murphy, L. (1962). The widening world of childhood . Basic Books.

National Association for the Education of Young Children. (No date). Build your public policy knowledge/Head Start . https://www.naeyc.org/our-work/public-policy-advocacy/head-start

Reichling, L. (2017). The Skinner Box. Article Library. https://blog.customboxesnow.com/the-skinner-box/

Peters, E. M. (2015). Child developmental theories: A contrast overview. Retrieved from https://learningsupportservicesinc.wordpress.com/2015/11/20/child-developmental-theories-a-contrast-overview/

Piaget, J. (1963). The origins of intelligence in children . Norton.

Piaget, J. (1967/1971). Biology and knowledge: An essay on the relations between organic regulations and cognitive processes . Trans. B. Walsh. University of Chicago Press.

Safran, J. D., & Gardner-Schuster, E. (2016). Psychoanalysis. In H. S. Friedman (Ed.), Encyclopedia of mental health (2nd ed., pp. 339–347). Elsevier. https://doi.org/10.1016/B978-0-12-397045-9.00189-0

Saracho, O. N. (2017). Literacy and language: New developments in research, theory, and practice. Early Child Development and Care, 187 (3–4), 299–304. https://doi.org/10.1080/03004430.2017.1282235

Saracho, O. N. (2019). Motivation theories, theorists, and theoretical conceptions. In O. N. Saracho (Ed.), Contemporary perspectives on research in motivation in early childhood education (pp. 19–42). Information Age Publishing.

Saracho, O. N. (2020). An integrated play-based curriculum for young children. Routledge/Taylor and Francis Group . https://doi.org/10.4324/9780429440991

Saracho, O. N., & Evans, R. (2021). Theorists and their developmental theories. Early Child Development and Care, 191 (7–8), 993–1001.

Scarr, S. (1992). Developmental theories for the 1990s: Development and individual differences. Child Development, 63 (1), 1–19. https://doi.org/10.2307/1130897

Schunk, D. (2021). Learning theories: An educational perspective (8th ed.). Pearson.

Shabani, K., Khatib, M., & Ebadi, S. (2010). Vygotsky’s zone of proximal development: Instructional implications and teachers’ professional development. English Language Teaching, 3 (4), 237–248.

Skinner, B. F. (1914). About behaviorism . Jonathan Cape Publishers.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . D. Appleton-Century Co.

Skinner, B. F. (1953/2005). Science and human behavior . Macmillan. Later published by the B. F. Foundation in Cambridge, Massachusetts.

Spodek, B., & Saracho, O. N. (1994). Right from the start: Teaching children ages three to eight . Allyn & Bacon.

Steiner, J. (2017). Lectures on technique by Melanie Klein: Edited with critical review by John Steiner (1st ed.). Routledge.

Strickland, C. E., & Burgess, C. (1965). Health, growth and heredity: G. Stanley Hall on natural education . Teachers College Press.

Thorndike, E. L. (1906). The principles of teaching . A. G. Seiler.

Torre, D. M., Daley, B. J., Sebastian, J. L., & Elnicki, D. M. (2006). Overview of current learning theories for medical educators. The American Journal of Medicine, 119 (10), 903–907. https://doi.org/10.1016/j.amjmed.2006.06.037

Vygotsky, L. S. (1934/1962). Thought and language . The MIT Press. (Original work published in 1934).

Vygotsky, L. S. (1971). Psychology of art . The MIT Press.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Harvard University Press.

Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20 (2), 158–177. https://doi.org/10.1037/h0074428

Weber, E. (1984). Ideas influencing early childhood education: A theoretical analysis . Teachers College Press.

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Saracho, O.N. Theories of Child Development and Their Impact on Early Childhood Education and Care. Early Childhood Educ J 51 , 15–30 (2023). https://doi.org/10.1007/s10643-021-01271-5

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Conceptual analysis article, the theoretical nature of systems thinking. perspectives on systems thinking in biology education.

• Faculty of Science, Department of Mathematics, Freudenthal Institute for Science and Mathematics Education, Utrecht University, Utrecht, Netherlands

Systems thinking has become synonymous to developing coherent understanding of complex biological processes and phenomena from the molecular level to the level of ecosystems. The importance of systems and systems models in science education has been widely recognized, as illustrated by its definition as crosscutting concept by the Next Generation Science Standards ( NGSS Lead States, 2013 ). However, there still seems no consensus on what systems thinking exactly implies or how it can be fostered by adequate learning and teaching strategies. This paper stresses the theoretical or abstract nature of systems thinking. Systems thinking is not just perceived here as “coherent understanding,” but as a learning strategy in which systems theoretical concepts are deliberately used to explain and predict natural phenomena. As such, we argue that systems thinking is not to be defined as a set of skills, that can be learned “one by one,” but instead asks for consideration of systems characteristics and the systems theories they are derived from. After a short elaboration of the conceptual nature of systems thinking, we portray the diversity of educational approaches to foster systems thinking that have been reported in the empirical literature. Our frame of analysis focuses on the extent to which attention has been given to the matching of natural phenomena to one of three systems theories, the integration of different systems thinking skills and the role of modeling. Subsequently, we discuss the epistemological nature of the systems concept and we present some conclusions on embedding systems thinking in the secondary biology curriculum.

Introduction

Modern science has made so many advances that the quantity of “basic” science to be taught in the classroom tends to increase every year. Therefore, current rapid developments in (life) sciences necessitate selecting a core set of essential and interconnected concepts and skills to serve as a basis for making sense of observable phenomena and for lifelong learning. This is underlined by among others the National Research Council (2011) , the NGSS Lead States (2013) , and the Dutch Board for the Innovation of Biology Education ( Boersma et al., 2010 ).

A core characteristic of biological sciences is that they deal with multiple levels of organization, e.g., molecule, cell, organ, organism, population, on which phenomena and processes occur and can be explained. To understand complex biological processes, different levels of organization should be distinguished and the relations within and between these levels should be elaborated ( Knippels, 2002 ; Verhoeff, 2003 ). The way of thinking to explain, understand and interpret complex and dynamic (biological) systems is called “systems thinking” ( Evagorou et al., 2009 ). According to NGSS Lead States (2013 , p.79) systems thinking can be seen as a crosscutting concept, namely “systems and system models,” that “ helps students deepen their understanding of the disciplinary core ideas, and that helps students to develop a coherent and scientifically based view of the world.”

Systems thinking is an important skill in different research fields, e.g., sociology, psychology, technology, meteorology, earth sciences, and biology. In this paper we will focus on systems thinking in biology (education). Several studies have reported on fostering students' systems thinking related to different biological topics, such as the human body as a system ( Ben-Zvi Assaraf and Orion, 2010 ; Tripto et al., 2016 , 2017 ), ecosystems ( Jordan et al., 2014 ), and the cell ( Verhoeff, 2003 ; Verhoeff et al., 2008 ). The diversity of these studies underlines the relevance and applicability of systems thinking in biology education at different educational levels. However, there is no consensus about which systems thinking skills students should achieve. According to Boersma et al. (2011) —who compared several empirical studies on systems thinking in science education—the differences can be attributed to whether the underlying systems theories are ignored or referred to implicitly or explicitly. A remarkable finding in their study was that four out of five analyzed studies did not include the identification of the system to be learned and the distinction of its (system) boundaries. In our view, this is questionable, as thinking back and forth between the system as a whole and its components is a key aspect of acquiring a coherent understanding of biological phenomena. Another notable difference between studies on systems thinking is that they differ in the extent to which they include modeling as an explicit part of systems thinking. In contrast to the other studies in Boersma's analysis, Verhoeff et al. (2008) for example, includes thinking backward and forward between general systems models and concrete biological objects and processes as an explicit element of systems thinking. The importance of modeling is also underlined by NGSS Lead States (2013 , p.80), who state that the crosscutting concept “systems and system models” is clearly related to the crosscutting practice “development and use of models.”

In this paper we emphasize the theoretical nature of the systems concept. Systems thinking is not just perceived here as “coherent understanding,” but as a cognitive skill in which systems theoretical concepts are deliberately used to explain and predict natural phenomena. As such, we argue that systems thinking is not to be defined as a subset of skills that can be learned “one by one.” Systems thinking asks for consideration of the systems characteristics of the three systems theories—such as the systems boundary, the vertical coherence between systems at different organizational levels or dynamicity—which has been referred to as “high-order systems thinking” ( Levy and Wilensky, 2008 ; Eilam and Reisfeld, 2017 ).

There are two reasons for writing this paper. First, the available empirical studies are very diverse in their perspectives on systems thinking, which makes it difficult to find some general trends in promoting systems thinking in biology education. Second, a closer analysis of empirical studies shows that learning outcomes are sometimes disappointing, while other studies present promising results regarding students systems thinking abilities ( Ben-Zvi Assaraf and Orion, 2005 ; Sommer, 2005 ; Evagorou et al., 2009 ). In an attempt to explain these different results, Boersma et al. (2011) questioned whether what was measured in all these studies should be considered as systems thinking. They claim that the different results of the empirical studies have to a large extent been caused by an imprecise definition of systems thinking and underestimation of its conceptual nature. The main aim of this paper is to provide a reasoned definition of systems thinking (perspectives), taking into account the different conceptual nature of the three systems theories. This will result in educational implications that may be useful for forthcoming empirical studies.

This paper addresses the following questions:

A. What is the conceptual nature of systems thinking?

B. What perspectives in systems thinking can be identified in empirical studies on systems thinking in biology education?

C. What perspective(s) in systems thinking can be recommended considering its conceptual nature, and what are the educational implications?

These questions are addressed by an analysis of empirical literature on systems thinking in biology education. First, we shortly elaborate the conceptual nature of systems thinking by presenting the key concepts from three systems theories. In section Systems Thinking in Biology Education, we analyze some empirical articles on systems thinking by reflecting on their perspectives on systems thinking. To portray the diversity of perspectives we carried out a more in-depth analysis of a small number of selected studies that used different approaches to implement systems thinking in biology education. Here, we present studies from four different research groups, i.e., Ben-Zvi Assaraf and Orion (2005 , 2010) , Hmelo et al. (2000 ; Hmelo-Silver et al., 2009) , Verhoeff (2003) ; Verhoeff et al. (2008) , and Eilam and Reisfeld (2017) . Subsequently, we discuss the epistemological nature of the systems concept, including the educational implications. Finally, we present conclusions on embedding systems thinking in the secondary biology curriculum.

The Conceptual Nature of Systems Thinking

We believe that the use of systems theory is conditional for successful application of systems thinking in a diversity of contexts. Consequently, developing students' systems thinking skill(s) should include the development of a systems concept. We are dealing with three systems theories, the GST, cybernetics and dynamic systems theory, each focusing on different aspects of biological systems. Each systems theory uses its own concepts (see Table 1 ), and the most basic of these concepts could be considered as cognitive tools in systems thinking.

Table 1. Three systems theories, their focus and key concepts.

Systems thinking has its roots in the organismic perspective of biologists at the beginning of the twentieth century. In order to grasp the differences between fixed and static machines and the dynamic processes of life, Bertalanffy launched his conception of the organism as “open system” in his General Systems Theory (GST). After the publication of the revised edition of von Bertalanffy (1968) his theory became a source for inspiration in many disciplines ( Gray and Rizzo, 1973 ). Systems were conceived as hierarchical and “Janus-faced” ( Koestler, 1973 ): The face turned toward the higher levels is that of the dependent and functional part; the face turned downward, toward its own constituents, is that of the whole of remarkable self-sufficiency in interaction with its environment.

In the 1970s the focus shifted to communication patterns in closed networks. Within this so-called Cybernetic perspective, living systems were conceived as (self)-regulating patterns of organization by means of non-linear causality. The major achievements of Cybernetics originated in comparisons between organisms and machines and led to the concepts of feedback and self-regulation ( Capra and Luigi Luisi, 2014 ). For a short while in the 1970s, Cybernetics was rather popular in ecological research since it seemed to provide an empirically testable theory. However, since cybernetics models are basically deterministic, there was not much correspondence between the values predicted by the model and the measured changes in open systems like in systems consisting of two populations ( Westra, 2008 ).

The dynamic systems theory or nonlinear systems theory has been linked to the development of powerful computers in the 1970s, as they opened the possibility to (mathematically) model the enormous complexity of life. Even the simplest form of life, a bacterial cell, is a highly complex network involving thousands of chemical processes. Now, nonlinear mathematics could be used to describe self-organization as a dynamic process, marking “a shift of perspective from objects to relationships, from measuring to mapping and from quantity to quality” ( Capra and Luigi Luisi, 2014 ). In current biological research, and other disciplines like meteorology and environmental science, modeling of processes in dynamic systems is frequently applied as a third way of research, besides empirical and theoretical research. The added value of systems modeling is the possibility to calculate the changes in open systems such as ecosystems and the biosphere. Systems modeling is grounded in dynamic systems theory and requires advanced mathematical methods.

The three described systems theories present a conceptual framework to understand biological phenomena. Systems thinking should not just be perceived as “coherent understanding,” but the descriptions above illustrate that theoretical concepts (see Table 1 ) are deliberately used to explain and predict natural phenomena. As such, we argue that systems thinking asks for consideration of systems characteristics and the systems theories they are derived from, such as the systems boundary or the vertical coherence between systems at different organizational levels that cannot in themselves be perceived by the senses. This raises the question to what extent attention has been given to matching natural phenomena to the three systems theories in empirical studies that promote systems thinking in biology education.

Systems Thinking in Biology Education

The increasing number of studies in systems thinking in primary and secondary biology education from a diversity of countries indicate that systems thinking is increasingly accepted as an educational objective. In the Netherlands, for example, it is included as a domain-specific skill in the examination programs for biology ( Boersma et al., 2010 ). Consequently, the studies categorized in section Categorization of the Diversity of Studies in Systems Thinking also consider systems thinking as a learning objective. Before we present some typical examples of empirical studies on systems thinking, we put them in a slightly broader context.

Empirical Studies on Systems Thinking

A group of Israeli researchers ( Ben-Zvi Assaraf and Orion, 2005 , 2010 ; Tripto et al., 2017 ) report on multiple empirical studies that follow students' systems thinking skills over time. The authors envision systems thinking as holistic understanding of these systems (they focused on the hydro-water cycle system and the human body system) as complex and functioning wholes. They offer a list of systems thinking skills from an elementary level, i.e., the ability to identify the components of a system and processes within that system; the ability to identify relationships among the system's components up to higher order systems thinking skills such as the ability to understand the cyclic nature of systems and thinking temporally. Students' concept maps are used to illustrate and externalize students' mental models of the systems involved to assess to what degree, and what level, systems thinking is achieved. The authors conclude that although students made meaningful progress in their systems thinking skills, the systems were still perceived as unrelated pieces of knowledges ( Tripto et al., 2017 ). For example, students did not use cellular level processes to explain phenomena on the level of the human body and most of the students did not make connections between the different body systems. The researchers stress the advantage of explicit scaffolding of systems thinking as a metacognitive strategy and explicit usage of “systems language” including terms like “interactions, patterns and dynamism, homeostasis, and hierarchy” ( Tripto et al., 2016 ).

A group of American researchers ( Hmelo et al., 2000 ; Liu and Hmelo-Silver, 2009 ; Hmelo-Silver et al., 2017 ) focused on helping Sixth grade children to acquire a deeper systemic understanding of the human respiratory system, including structural, behavioral, and functional relations. They used a function-centered conceptual representation emphasizing the function and behavior of the human respiratory system. The authors conclude that, although the students tended to map the respiratory system across different organizational levels, examining one complex system is not enough for improving learners' global mental models reflecting the dynamics of (all) biological systems ( Liu and Hmelo-Silver, 2009 ). Recently, a refined conceptual representation was presented by Hmelo-Silver et al. (2017) reflecting the mechanistic reasoning of ecosystem learning, termed Components, Mechanisms, and Phenomenon (CMP). The representation was used as a framework to help students organize their ideas before they engaged in model development. The combination of a conceptual representation with the explicit practice of modeling allowed learners to externalize their thinking and collaboratively discuss their ideas. The results suggest that the approach helps students to deepen their understanding of systems and to extend their ecosystem learning beyond a particular context. This is due to the fact that the students are reasoning about system elements in a generic way. This process of abstraction allows students to relearn system ideas in novel contexts, because students can use the CMP frame as cognitive support.

A group of Dutch researchers ( Verhoeff, 2003 ; Verhoeff et al., 2008 , 2013 ; Westra, 2008 ) describe a similar process of abstraction. They present two modeling approaches to facilitate students' conceptual understanding of the topic at hand and understanding the way different representations are instrumental in acquiring this understanding (metacognition). In the first approach ( Verhoeff et al., 2008 ), upper-secondary students were actively engaged in constructing and revising cellular models and comparing familiar representations of biological phenomena to more abstract system models, based on the GST. In a subsequent approach, students were involved in a sequence of computer modeling activities to clarify the dynamics of ecosystem behavior at the level of the organism, population and ecosystem ( Westra, 2008 ). Based on the empirical results of the study, Westra concludes that coherent understanding of complex biological systems needed more explicit matching of system characteristics, i.e., systems boundary, vertical coherence, to empirical phenomena. They conclude that coherent understanding of complex systems requires explicit attention for navigation between the levels of organization and also for the stepwise change from concrete models to models of higher abstraction.

Recently, Eilam and Reisfeld (2017) designed a simulation-based curriculum in which 16 students (14–15 years old) navigated the levels of organization both downward and upward by means of two contrasting simulations. A System Dynamics-based simulation offered a macro view on the population as a whole entity and an Agents-based simulation provided a perspective on the population's single agents' behavior. The authors focused on cognitive aspects involved in the manipulation of each of the two simulation types, explicitly contrasting their simulation output. In line with Verhoeff et al. (2008) the authors conclude that shifting between complementary multiple representations of the macro- and micro-level scaffolds students' complex systems thinking, i.e., understanding the visualization of the macro-level as emerging from the micro-level interactions occurring among the system components. In addition, the dynamic and quantitative nature of the simulations and the graphs they produced, improved students' stochastic thinking and their ability to perceive biological phenomena as a series of complex events that occur simultaneously over time.

We started by the assumption that although systems thinking is an accepted educational objective in biology education, no single definition of systems thinking becomes apparent, nor consensus how it can be fostered by adequate learning and teaching strategies. It is remarkable that many studies ( Verhoeff et al., 2008 ; Hmelo-Silver et al., 2017 ; Tripto et al., 2017 ) highlight the development of systems language, while (except for Verhoeff et al., 2008 ) no explicit reference is made to one of the systems theories from which the vocabulary has been derived from. Apart from the conceptual nature of systems thinking, there is a remarkable difference to the extent that modeling is conceived as a central tool and/or aspect of systems thinking. In the next section, we categorize and compare the core ideas of four typical educational approaches to systems thinking representing the empirical literature sketched above.

Categorization of the Diversity of Studies in Systems Thinking

Many lists of systems thinking skills have been presented in the educational literature ( Booth Sweeney and Sterman, 2000 ). Here, we aim to provide an overview of different perspectives that have been reported in empirical studies on systems thinking. Our overview includes a more in-depth analysis of four of these studies, as they represent rather particular portrayals of systems thinking.

As a first step in categorizing perspectives on systems thinking we focused on the source and nature of the learning objectives that have to be attained by learners. Learning objectives could either be defined as a set of cognitive skills derived from systems theories as outlined in section The Conceptual Nature of Systems Thinking or as behavioral actions with no direct link to a systems theory:

1. Behavioral perspective . Systems thinking is defined by a mixed set of learning objectives. Some objectives refer to one or more systems characteristics (such as components, or interaction between systems components), while others do not and only indicate a behavioral component ( Ben-Zvi Assaraf and Orion, 2005 , 2010 ). Systems characteristics do frequently not originate from systems theory, and some characteristics of systems that are recognized in systems theory may be missing.

2. Cognitive skills perspective . Systems thinking is defined by a set of learning objectives with one or more systems characteristics generally derived from systems theory ( Verhoeff, 2003 ; Sommer, 2005 ; Verhoeff et al., 2008 ; Evagorou et al., 2009 ) A set of “cognitive skills” makes it clear that the focus is on application of the systems concept in exploring and analyzing complex biological systems, not just on developing conceptual understanding of the systems concept (e.g., Eilam and Reisfeld, 2017 ; Hmelo-Silver et al., 2017 ), i.e., higher order systems thinking.

A second distinction between perspectives on systems thinking is whether systems thinking is defined as a set of sub skills that can be acquired independently and have their own value in understanding biological phenomena, or that systems thinking should only be seen as meaningful when it constitutes a coherent system of skills and concepts that emphasize the system as a whole:

3. Component perspective . In the component perspective the skills or conceptual components defining systems thinking are considered as parts that can be acquired (and scored) independently, while these parts together do define systems thinking ( Ben-Zvi Assaraf and Orion, 2005 ; Sommer, 2005 ). The focus is not on how the components can be integrated into a systems concept or an integrated systems thinking skill.

4. Holistic perspective . In the holistic perspective the system as a whole is emphasized, and complex systems learning is aimed at understanding biological phenomena as emerging from the dynamic interactions between components across different levels of organization. This requires students to recognize these phenomena as “systems” ( Verhoeff et al., 2008 ; Boersma et al., 2011 ; Eilam and Reisfeld, 2017 ; Hmelo-Silver et al., 2017 ).

Modeling is central to the crosscutting concept of systems and systems models, so a third way of categorizing studies is based on the representation of biological phenomena; either as qualitative or as quantitative models:

5. Qualitative modeling perspective . In a qualitative modeling perspective a systems model is developed as an abstract representation of a system, showing some systems characteristics ( Verhoeff et al., 2008 , 2013 ). The model is used in thinking forward and backward between systems and biological objects.

6. Quantitative modeling perspective . A quantitative modeling perspective focuses on the prediction of quantitative changes in biological systems ( Westra, 2008 ; Eilam and Reisfeld, 2017 ; Hmelo-Silver et al., 2017 ). Computer tools for quantitative modeling are grounded in dynamic systems theory, although that is not evident for the students because the mathematics underlying the calculations is not accessible.

The perspectives of four typical empirical studies on systems thinking are identified and discussed below.

Ben-Zvi Assaraf and Orion (2005)

The study presents the results of students' advances in systems learning in junior high school when working on a module about the water cycle. The authors define systems thinking as a set of system-thinking skills in the context of the water cycle system without referring explicitly to a systems theory. The systems boundary is not indicated, so that it seems that implicitly the water cycle of the whole earth is considered. A figure showing a diagrammatic representation of the water cycle includes all components, lacks a systems boundary and does not consider the possibility that some parts may only be found in specific areas.

Some of the system-thinking skills enclose a cognitive component of systems theory (systems, components, but considering relationships instead of dynamical interaction). However, a skill referring to the ability to recognize a set of components as a whole is not included, although most skills include the systems concept. General characteristics of systems are not identified. Consequently, the conceptual components in the study are the components of the water cycle, and not the components of general systems. The lack of a holistic perspective and clear system boundaries might have hindered students' integration of the different components into a systems concept and their understanding of different systems levels. Since only few data are presented about learning and teaching processes, it is unclear whether components of the water cycle were indicated with terms that are linked to a certain systems theory.

Categorization

Systems thinking is defined as a set of skills, although most skills include a cognitive component (1.Cognitive skills perspective) . Furthermore, students are invited to construct a water cycle from its constituting parts (3 . Component perspective ). Modeling is not explicitly addressed as part of systems thinking.

Verhoeff et al. (2008)

The study aims to develop coherent cell biological knowledge, and an initial model for systems thinking in upper secondary education by means of an emergent modeling strategy. Systems thinking is considered as a metacognitive skill consisting of a limited set of partial skills. The study is grounded in the GST. The first part of the educational strategy, focuses on the development of coherent understanding of the cell by means of a sequence of two- and three-dimensional system models with increasing abstraction. In the second part of the strategy a computer program is used to show how a general systems model can be developed by further abstraction of models of cells, embedded in organs and an organism. The qualitative model that is developed represents several levels of biological organization, interaction between components, a systems boundary, and the open nature of all included systems ( Verhoeff et al., 2013 ). Finally, students apply this general hierarchical systems model to the topic of breast-feeding. Although the testing procedure was restricted, the intermediate learning outcomes including the last assignment about breast-feeding were in accordance with was expected. Consequently, it was concluded that the strategy is feasible for students and teachers and may result in the development of coherent cell biological knowledge when integrated in systems thinking.

Systems thinking is defined as a set of cognitive skills, derived from the GST ( 2. Cognitive skills perspective ). The study focuses on the cell and the system as a whole ( 4. Holistic perspective ) and uses a qualitative model ( 5. Qualitative modeling perspective ).

Hmelo-Silver et al. (2017)

Hmelo-Silver et al. (2017) report a quasi-experimental study on an 8-week classroom intervention to support students' reasoning about ecosystems in the seventh grade. Students were engaged in modeling and simulations with a conceptual representation termed Components, Mechanisms, and Phenomenon (CMP). The CMP conceptual representation intended to “ support learners in framing systems thinking around a particular phenomenon or ecological pattern (P); encouraging learners to generate or recall plausible mechanisms (M) that may result in the (P); and explore the parts or components (C) that interact to result in (M and P).” The CMP conceptual framework reflects the mechanistic reasoning of ecosystem learning and is used as an indicator of systems thinking, i.e., “dynamic and multi-leveled thinking.” Dynamic thinking is further specified as the identification of components in relation to mechanisms and behavior. The Mechanisms here refer to the underlying ecosystem processes (decomposition) and serve as a mediator between Phenomena (dying fish in a pond) and Components (fish, plants, bacteria). Multi-level thinking is specified as the identification of relationships between macro and micro structures or processes. During the intervention students were asked to explain a Phenomenon, e.g., dead and dying fish in a pond. Their inquiry was guided by CMP-based hypermedia, and simulations were used to provide opportunities for students to engage with evidence and mechanisms that underlie the phenomenon at different scales. The results of the study show that students who engaged in the intervention deepened their understanding of ecosystem dynamics compared to students who engaged in traditional instruction. The authors suggest that the use of the combination of a conceptual representation and modeling practices helps students to deepen their understanding of systems and to extend their ecosystem learning beyond a particular context. This is due to the fact that the students are reasoning about system elements in a more generic way. This process of abstraction allows students to relearn system ideas in novel contexts, because students can use the CMP frame as cognitive support.

Systems thinking is defined as a combination of conceptual knowledge and dynamic and multi-leveled thinking (2. Cognitive skills perspective ). The study aims to promote holistic understanding of system elements, i.e., underlying Components and Mechanisms that explain a complex Phenomenon at a macro level, in a more generic way (4. Holistic perspective ). The model based approach includes qualitative CMP conceptual representations (5. Qualitative modeling perspective ). By means of computer based simulations students can gather evidence and raw data to refine their models' plausibility and parsimony to support/refute their ideas. (6. Quantitative modeling perspective ).

Eilam and Reisfeld (2017)

The authors present an innovative curriculum unit for promoting complex systems thinking in which students manipulate and contrast two simulations of population growth, i.e., a system dynamics model presenting the macro level and an agent-based model presenting the micro level. The System Dynamics model expresses the temporal cause-and-effect relationships between variables without directly representing the involved agents. It enables learners' to explore the macro level of population dynamics and present a graph representing quantitative interactions of variables within a system, stock and flow. The Agent Based-simulation simulates qualitatively the interaction between the different agents (individuals) of the system (population) and between the agents and their environment. This enables a better understanding of the variability and causality of the system at the micro level. By means of computerized experiments and analysis of self-constructed graphs, students examined the system's mechanisms and behaviors, while considering both macro and micro levels. The authors mention that prior to these experiments students raised research questions regarding the manipulation of the system behavior: “How would the system behave if the initial population size increased?” illustrating the conceptual nature of “systems thinking.” After using both simulation perspectives, students were asked to compare between the output of the System Dynamics based and Agent based simulations and explain the differences. The joint use of the two perspectives offered by the simulations contributed to increasing students' ability to relate between the micro and macro levels of a complex system, as well as stochastic thinking and dynamics involved in achieving equilibrium, i.e., students' higher order systems thinking. In addition, the curriculum increased conceptual knowledge of the systems' basic components, the relations between these components, and of the process of population growth (i.e., stock, flow, interactions, equilibrium, etc.). The authors do suggest to include more varied and complex systems in students' inquiry, to enable them in using the simulations as cognitive tools in learning about complex systems.

Systems thinking is defined as a combination of conceptual knowledge and thinking modes (2. Cognitive skills perspective ). The study aims to promote holistic understanding of the behavior of a complex system requiring comprehension of many system aspects, like the components, their interactions and equilibrium processes (4. Holistic pers pective). The model/based approach includes both qualitative agent-based animations representing population growth (5. Qualitative modeling perspective ) and quantitative modeling, based on the Lotka-Volterra prey-predator differential equations (6. Quantitative modeling perspective ).

The analysis of the perspectives on systems thinking of the four selected studies is summarized in Table 2 . The table shows that in the study of Ben-Zvi Assaraf and Orion (2005) two of the six perspectives were recognized, and respectively three, four and four of the perspectives in the studies of Verhoeff et al. (2008) , Hmelo-Silver et al. (2017) and Eilam and Reisfeld (2017) .

Table 2. Perspectives on systems thinking identified in the four selected studies.

The Epistemological Nature of the Systems Concept

The previous section shows that, except for the study of Ben-Zvi Assaraf and Orion (2005) , there is remarkable similarity between the studies on three aspects: systems thinking is presented as understanding complex systems as a whole (1); it is regarded as a set of “higher order cognitive skills” (2), that are learned by the application of (multiple) representations or models (3). In these studies, all models represent an underlying systems concept, i.e., the hierarchical systems concept in the study of Verhoeff et al. (2008) , the Component Mechanisms Phenomenon concept of Hmelo-Silver et al. (2017) , and the Dynamical Systems concept in the study of Eilam and Reisfeld (2017) . Notably, only Verhoeff et al. explicitly refer to an underlying systems theory, i.e., GST.

We found no empirical studies that focused on the system as a theoretical concept . However, Boersma (2016) proposed to reconsider the epistemological nature of the systems concept to emphasize that it is derived from systems theory and not from the process of abstracting (multiple) empirical phenomena. To clarify why it is sensible to consider the systems concept as a theoretical concept, we can consider a system with an ill-defined systems boundary, like a marine ecosystem such as the North Sea. In such ecosystems a boundary is concluded from observable organisms and their interactions, although its exact spatial position may be discussed. Nevertheless, we consider the North Sea to be an ecosystem. Seeing an ecosystem as a whole, also when it is not visible, implies that an ecosystem actually is a mental construct that allows us to recognize entities and to predict a number of empirical phenomena. We may predict, for example, that an ecosystem maintains its identity, that feedback mechanisms occur between populations and that it is self-organizing over time. The mental construct that we may consider an ecosystem as a whole may find its origin in a theory like systems theory. Systems theory is a mental construct which describes the characteristics of theoretical objects and not of empirical objects. Actually, systems thinking implies framing of empirical phenomena from a systems perspective. In other words, systems theoretical knowledge shows a coherence between phenomena which are not accessible to direct observation. This implies a distinction between theoretical and empirical concepts [ Hempel (1966/1973) ( Koningsveld, 1987 ; Van Aalsvoort, 2000 )]. Empirical concepts are then defined as abstractions of empirical phenomena, while theoretical concepts are derived from an axiomatic theory like systems theory, or particle theory. It should be noted that in this line of reasoning the term theory has a more restricted meaning than in daily practice and unfortunately also in many scientific studies, where it is frequently used as another term for pattern, regularity or what has been indicated as “law of nature.”

Distinguishing theoretical from empirical concepts inevitably evokes the question how to define the relation between both. Figure 1 presents two lines of relations, i.e., a developmental line (starting from the top left box) and the application line (starting from the top right box). The developmental line focuses on the development of theoretical concepts like the system boundary. Theoretical concepts are not developed empirically, but can be seen as parts of webs of belief that together provide a theoretical perspective. A historical example of the development of a scientific theory is the invention of the structure of benzene by Kekulé (1829–1896) who derived his theory about the cyclic structure of benzene from two dreams ( Rocke, 2010 ). This illustrates 1) that formation of a theory requires extensive prior knowledge of the domain the theory refers to, and 2) scientific theories are not just developed by abstraction of empirical phenomena, but require a source from outside, such as dreams, fantasies, metaphors, or a seemingly unrelated domain of knowledge.

Figure 1. The relations between empirical concepts and theoretical concepts. The model illustrates a gap between theoretical conceptions and concrete events. This gap can only be crossed by means of bridge principles after Boersma (2016) .

Application of a theory to explain empirical phenomena asks for a connection between concepts from two different languages, and this can be accomplished via bridging principles or rules of correspondence ( Koningsveld, 1987 ; Van Aalsvoort, 2000 ). Bridging principles are meant to connect a theory and its observational basis. Without these principles a theory would be scientifically meaningless, since there is no way of testing it empirically. In the example of the North Sea it means that individual organisms or populations, concepts of our empirical language, are compared with systems components. The line of reasoning is then: “If populations are understood as components of an ecosystem…; followed by an empirically testable statement, such as:…; the size of a population is depending (to some extent) on the size of one or more other populations.”

A theory cannot provide explanations, since theory and empiricism use different languages that cannot be combined in chains of causal reasoning. A theory can suggest empirically testable predictions, and if affirmed the theory can contribute to causal chains in empiricism. Confirmation of predictions does not count as a proof of the theory, since there is no causal relation between the empirical and theoretical knowledge domains. Consequently, a scientific theory is not true or proven (or dismissed), but functional (or dysfunctional) as long as it generates empirically testable predictions. Of course empirical outcomes may differ from what was predicted, and if methodological mistakes are excluded, it may be concluded that the theory should be adapted or even rejected. The ultimate test is the design of experiments with the purpose to test or falsify the functionality of the scientific theory ( Popper, 1963 ).

Educational Implications of the Theoretical Nature of Systems Thinking

Understanding complex living systems involves integration of the key theoretical concepts of the three systems theories summarized in section The Conceptual Nature of Systems Thinking. It refers to thinking about multiple interdependent levels, nonlinear causality and emergence ( Jacobsen and Wilensky, 2006 ). Understanding such concepts is difficult as they cannot (simultaneously) be perceived by the senses and students often have difficulty in thinking beyond linear relationships, single causality and visible structures. A strategy to overcome these difficulties might be to start with one systems perspective, guided by conceptual representations or models ( Verhoeff et al., 2013 ; Hmelo-Silver et al., 2017 ). Such representations can promote understanding of the systems interrelationships by highlighting key aspects of a certain system. For example, a model based on the GST highlights the hierarchal structure of living systems, while a Lotka-Volterra model highlights the dynamics of interspecific competition. Thinking back and forth between real biological phenomena and a model based on a specific systems theory might be a first step toward systems thinking. “Higher order systems thinking” could then be described at the metacognitive level, where students can navigate between models based on different systems theories and employ them to understand biological phenomena. Looking at our analysis in section The Conceptual Nature of Systems Thinking, it is striking that three studies feature systems modeling strategies that clearly have a theoretical basis Eilam and Reisfeld (2017) , Hmelo-Silver et al. (2017) , and Verhoeff et al. (2008) . Each of these studies reports that additional attention should be given to the practice of modeling and how it fosters conceptual understanding of biological phenomena. Eilam and Reisfeld (2017 , p. 57). for example stress that students may need “more time, higher number and diversity of manipulation experiences and examples (…) of varied complex systems.”

Our argument that systems concepts should be classified as theoretical concepts has several implications for determining an appropriate educational strategy. The most important implication is evidently that when students have to learn systems thinking, they have to learn to apply a systems theory (or model) as a coherent “holistic” “framework” in which the “key concepts” become “meaningful.” Consequently, our next question is what this reclassification of system concepts and models implies for the selection of systems perspectives and educational strategies that aim for higher order systems thinking.

Promising Perspectives on Systems Thinking

Considering the perspectives on systems thinking identified in section Categorization of the Diversity of Studies in Systems Thinking, the reclassification of the systems concept implies that the cognitive nature of systems thinking is emphasized and that a cognitive skill perspective is desirable. Furthermore, since key systems concepts are embedded in a specific systems theory, applying a holistic approach becomes unavoidable. Systems thinking should focus primarily on the development of the theoretical systems concept, and not just on understanding individual concepts or sub skills, such as identifying the components of a system. Thus, the crucial step in systems learning is the step from empirically observable phenomena to a systems theoretical conceptualization of such phenomena. The three studies in our analysis that feature qualitative modeling indicate that a qualitative modeling strategy in which complementary multiple representations are used may be helpful in the development of an initial systems concept. Verhoeff et al. (2008) have suggested to derive such a first systems concept from the GST as in biology systems are quite often identified as structural organizations, i.e., a “cell as a system” and a “population as a system.” Also Ben-Zvi Assaraf and Orion (2005) , Eilam and Reisfeld (2017) , and Hmelo-Silver et al. (2017) start with identification of the structural components involved in the phenomenon to be studied. Such “systems” are still quite abstract; it is not possible to make a photograph of a “cell as a system.” Consequently, the question arises how a “cell as a system” can be represented pictorially. The most appropriate representation of a system seems to be a hierarchical systems model, derived from the GST ( Verhoeff et al., 2008 , 2013 ). Therefore it is recommended to select a qualitative modeling approach, in the initial development of systems thinking. As the studies of Eilam and Reisfeld (2017) and Hmelo-Silver et al. (2017) illustrate, deeper understanding of the dynamic nature of systems could be furthered by a quantitative modeling approach. However, in our view, introducing students to the “theoretical” vocabulary of cybernetics and/or dynamical systems theory in which these quantitative models are embedded ( Tripto et al., 2017 ) needs to be carefully guided by bridge principles.

The Distinction Between Empirical and Theoretical Concepts

In section The Epistemological Nature of the Systems Concept we concluded that theoretical and empirical concepts are connected by bridge principles. In other words, bridge principles propose a connection between two languages, i.e., an empirical language describing observations and a theoretical language using systems theoretical concepts. To avoid conceptual confusion it is necessary to distinguish these two languages and to allow students to grasp the benefits of explicit usage of “systems language” including terms like “interactions, patterns and dynamism, homeostasis, and hierarchy.” This raises the question how to distinguish the two languages in the biology classroom. In current biology textbooks, empirical and theoretical concepts seem to be hardly distinguished at all and the theoretical nature of some concepts is not recognized, so that almost only empirical language is used. Many textbooks, for example, use the concept “population,” but it is unclear whether it refers to an (observable) collection of organisms of the same species, living in a specific area, or to a population as a system. Although no empirical studies are available, it seems obvious that both biology teachers and authors of biology textbooks implicitly support the view that the systems concept is a further abstraction of empirical concepts. Consequently, systems models are considered implicitly as abstract representations of biological objects ( Verhoeff et al., 2013 ; Hmelo-Silver et al., 2017 ) as well as representations of a specific systems theory. To overcome conceptual confusion, we propose to explicitly address the difference between theoretical models and empirical models as a bridge principle. Figure 2 indicates which models can be used to avoid confusion.

Figure 2. Proposed notation of theoretical and empirical models.

This is in line with suggestions by Hmelo-Silver et al. (2017) , albeit that they do not stress the theoretical basis of systems thinking. They propose that explicit introduction of the language of their CMP-framework fosters students' reasoning about complex systems. They also underline the importance of activities that explicitly support systematic thinking about systems and models.

Conclusion and Discussion

In this paper we presented systems thinking as a cognitive skill in which systems theory is deliberately used to explain and predict natural phenomena that cannot per se be observed empirically. This means that in the development of students' systems thinking we should include bridging principles, which we have articulated as explicitly introducing students to the theoretical basis of systems models (and key concepts): If we approach the North Sea from a GST perspective, we can identify the systems borders and the systems components with their interactions within a framework of functional relationships.

Available empirical studies are very diverse in their perspectives on systems thinking. Nevertheless, a closer categorization of perspectives on systems thinking in four studies revealed some similar trends in promoting systems thinking in biology education: (1) Systems learning should focus on application of a systems concept in exploring and analyzing complex biological systems (cognitive skills perspective). (2) Emphasizing the need to focus on the conceptual core of systems theory also implies that attention should be given to the systems boundary. Students should learn to see a system as a whole (holistic perspective), and not only as a network of interacting components. (3) Using systems models may support students' conceptual development of the systems concept. Consequently, it was recommended to select the qualitative modeling perspective. Quantitative modeling is not recommended in developing an initial systems concept, because it requires substantial effort from both students and teachers and may result in less explicit attention to the development of the systems concept (i.e., bridge principle).

The conceptual core of systems thinking, was elaborated in two parts. First, we presented a set of key concepts from each of the three systems theories: the GST, Cybernetics and the Dynamical systems theory. We proposed to consider this set of systems concepts as the focus of students' conceptual development in systems thinking. And second, the epistemological nature of systems theory was considered. We argued that systems theories and their concepts are theoretical and cannot be developed or learned just by further abstraction of empirical phenomena. As a consequence, empirical and theoretical concepts should be explicitly distinguished in learning about systems.

In this paper we only discussed the initial step in the development of systems thinking. Since systems thinking is considered here as a metacognitive skill, students should also have the opportunity to practice it in a subsequent learning trajectory, as also strongly suggested by Eilam and Reisfeld (2017) and Hmelo-Silver et al. (2017) . Since there are three different systems theories, each focusing on different characteristics, a trajectory aiming for development of a complete systems concept should include all three. However, a systems concept integrating all three systems theories might be too complex. A further complication of developing such a concept is that no model seems available that represents the characteristics of all three theories. Therefore, we recommend introducing the systems concept in three steps, coinciding with the three systems theories. An initial systems concept could be derived from the GST, since it considers biological systems as open systems. Following the course of history, Cybernetics and the Dynamical systems theory could follow. Another option may be to first focus on the development of some basic empirical components of systems that can be directly observed or link to students' prior empirical knowledge. For example, although students in secondary education will not have developed a theoretical concept of populations, they can recognize that the size of a group of organisms in predator–prey relationships will be influenced by groups of other species. That may result in the development of an initial concept of feedback at the empirical level (and not at the theoretical level). The question remains whether it is desirable to focus first on the development of a theoretical systems concept, or on the development of preparatory empirical concepts like community, population and feedback. Finally, to develop an understanding of all three systems theories that together form a complete systems concept, students should have the opportunity to practice each of them. They should explore all three theoretical system conceptualizations at different levels of biological organization, with varied complex phenomena and maybe also in other disciplines than biology, such as chemistry, developmental psychology, and environmental science ( Eilam and Reisfeld, 2017 )

A complete program of systems thinking in biology education, and as a crosscutting concept, in adjacent disciplines, cannot yet be realized with current understanding of how students can master “higher order” systems thinking. Suggestions from this paper and from the analyzed studies should be reconsidered, elaborated and tested in educational practice. Although it is not yet possible to sketch the outlines of one or more complete programs focusing on the full complexity of systems thinking, it is worthwhile to strive for, as systems thinking provides students with a useful cognitive toolbox for considering complexity in a variety of domains.

The development of such a program in systems thinking would require a lot of teaching time in biology education. At first glance, that may conflict with already overloaded current educational programs. At the moment, biology curricula have to comply with a number of conflicting demands, such as reducing curricular overload, teaching according to a developmental perspective, and elaborating coherence with other science subjects. It would seem that some choices will have to be made, and a restructuring of biology education according to systems thinking might well offer an attractive alternative.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ben-Zvi Assaraf, O., and Orion, N. (2005). Development of system thinking skills in the context of earth system education. J. Res. Sci. Teach. 42, 518–560. doi: 10.1002/tea.20061

CrossRef Full Text | Google Scholar

Ben-Zvi Assaraf, O., and Orion, N. (2010). Four case studies, six years later: developing system thinking skills in junior high school and sustaining them over time. J. Res. Sci. Teach. 47, 1253–1280. doi: 10.1002/tea.20383

Boersma, K. (2016). Bouwstenen Voor Begripsontwikkeling in Het Biologieonderwijs. [Building Blocks for Conceptual Development in Biology Education]. Utrecht: Nederlands Instituut voor Biologie.

Boersma, K. T., Kamp, M. J. A., Van den Oever, L., and Schalk, H. H. (2010). Naar Actueel, Relevant en Samenhangend Biologieonderwijs. Eindrapportage van de commissie Vernieuwing Biologie Onderwijs, met nieuwe examenprogramma's voor havo en vwo. [Towards actual, relevant and coherent biology education. Final report of the board for the innovation of biology education, with new examinations programs for general upper secondary and pre-university education]. Utrecht: CVBO.

Google Scholar

Boersma, K. T., Waarlo, A. J., and Klaassen, K. (2011). Rethinking the introduction of systems thinking in biology education. J. Biol. Educ. 45, 190–197. doi: 10.1080/00219266.2011.627139

CrossRef Full Text

Booth Sweeney, L. B., and Sterman, J. D. (2000). Bathtub dynamics: initial results of a systems thinking inventory. Syst. Dyn. Rev. 16, 249–286. doi: 10.1002/sdr.198

Capra, F., and Luigi Luisi, P. (2014). The Systems View of Life: A Unifying Vision. Cambridge: Cambridge University Press.

Eilam, B., and Reisfeld, D. (2017). A curriculum unit for promoting complex system thinking: the case of combined system dynamics and agent based models for population growth. J. Adv. Educ. Res. 2, 39–60. doi: 10.22606/jaer.2017.22001

Evagorou, M., Korfiatis, K., Nicolaou, C., and Constantinou, C. (2009). An investigation of the potential of interactive simulations for developing system thinking skills in elementary school: a case study with fifth-graders and sixth-graders. Int. J. Sci. Educ. 31, 655–674. doi: 10.1080/09500690701749313

Gray, W., and Rizzo, N. (1973). Unity through Diversity. A Festschrift for Ludwig von Bertalanffy . New York; London; Paris: Gordon and Breach Science Publishers.

Hempel, C. (1966/1973). Philosophy of Natural Science . Englewood Cliffs: Prentice Hall Inc.

Hmelo, C. E., Holton, D. L., and Kolodner, J. L. (2000). Designing to learn about complex systems. J. Learn. Sci. 9, 247–298. doi: 10.1207/S15327809JLS0903_2

Hmelo-Silver, C. E., Jordan, R., Eberbach, C., and Sinha, S. (2017). Systems learning with a conceptual representation: a quasi-experimental study. Instruc. Sci. 45, 53–72. doi: 10.1007/s11251-016-9392-y

Hmelo-Silver, C. E., Lui, L., and Jordan, R. (2009). Visual representation of a multidimensional codingscheme for understanding technology-mediated learning about complex natural systems. Res. Pract. Technol. Enhan. Learn. Environ. 4, 253–280. doi: 10.1142/S1793206809000714

Jacobsen, M. J., and Wilensky, U. (2006). Complex systems in education: scientific and educational importance and implications for the learning sciences. J. Learn. Sci. 15, 11–34. doi: 10.1207/s15327809jls1501_4

Jordan, R. C., Brooks, W. R., Hmelo-Silver, C., Eberbach, C., and Sinha, S. (2014). Balancing broad ideas with context: an evaluation of student accuracy in describing ecosystem processes after a system-level intervention. J. Biol. Educ. 48, 57–62. doi: 10.1080/00219266.2013.821080

Knippels, M. C. P. J. (2002). Coping With the Abstract and Complex Nature of Genetics in Biology Education. Utrecht: CDß Wetenschappelijke reeks. Dissertation Utrecht University.

Koestler, A. (1973). “The tree and the candle,” in Unity through diversity. A Festschrift for Ludwig von Bertalanffy , eds W. Gray and N. D. Rizzo (New York, NY: Gordon and Breach Science Publishers), 287–314.

Koningsveld, H. (1987). Het Verschijnsel Wetenschap . Inleiding tot de wetenschapsfilosofie. [The phenomenon of science. Introduction to the philosophy of science]. Amsterdam; Meppel: Boom.

Levy, T. S., and Wilensky, U. (2008). Inventing a ‘mid-level’ to make ends meet: reasoning between the levels of complexity. Cogn. Instr. 26, 1–47. doi: 10.1080/07370000701798479

Liu, L., and Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. J. Res. Sci. Teach. 46, 1023–1040. doi: 10.1002/tea.20297

National Research Council (2011). A framework for K-12 science education: practices, crosscutting concepts, and core ideas . Washington, DC: The National Academies Press.

NGSS Lead States (2013). Next Generation Science Standards: For States, by States. Achieve, Inc. on Behalf of the Twenty-Six States and Partners That Collaborated on the NGSS , 1–103. doi: 10.17226/18930

PubMed Abstract | CrossRef Full Text

Popper, C. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge . London: Routledge and Kegan.

Rocke, A. J. (2010). Image and Reality. Kekulé, Kopp, and the Scientific Imagination. Chicago: Chicago Press.

Sommer, C. (2005). Untersuchung der Systemkompetenz von Grundschulern im Bereich Biologie. [Study on the Primary Students' Systems Competence of in Biology] . Dissertation, Christian-Albrechts-Universität Kiel/Germany.

Tripto, J., Ben-Zvi Assaraf, O., Snapir, Z., and Amit, M. (2016). A reflection interview – “What is a system” as a knowledge integration activity for high school students' understanding of complex systems in human biology. Int. J. Sci. Educ. 38,564–595. doi: 10.1080/09500693.2016.1150620

Tripto, J., Ben-Zvi Assaraf, O., Snapir, Z., and Amit, M. (2017). How is the body's systemic nature manifested amongst high school biology students? Instruc. Sci. 45, 73–98. doi: 10.1007/s11251-016-9390-0

Van Aalsvoort, J. G. M. (2000). Chemistry in Products. A Cultural-Historical Approach to Initial Chemical Education . Dissertation, Utrecht University.

Verhoeff, R. P. (2003). Towards Systems Thinking in Cell Biology Education . Utrecht: C.D.ß Wetenschappelijke reeks (dissertation).

Verhoeff, R. P., Boersma, K. T., and Waarlo, A., J. (2013). “Multiple representations in modeling strategies for the development of systems thinking in biology education,” in Multiple Representations in Biological Education. eds D. F. Treagust and Chi-Yan Tsui (Dordrecht; Heidelberg; New York, NY; London: Springer), 331–348.

Verhoeff, R. P., Waarlo, A. J., and Boersma, K. T. (2008). Systems modelling and the development coherent understanding of cell biology. Int. J. Sci. Educ. 30, 543–568. doi: 10.1080/09500690701237780

von Bertalanffy, L. (1968). General System Theory. Foundations, Development, Applications. New York, NY: Braziler.

Westra, R. (2008). Learning and Teaching Ecosystem Behavior in Secondary Education . Utrecht: C.D.ß Wetenschappelijke reeks (dissertation).

Keywords: systems thinking, systems theory, modeling, biology education, qualitative analysis, coherent understanding, cognitive skill

Citation: Verhoeff RP, Knippels MCPJ, Gilissen MGR and Boersma KT (2018) The Theoretical Nature of Systems Thinking. Perspectives on Systems Thinking in Biology Education. Front. Educ . 3:40. doi: 10.3389/feduc.2018.00040

Received: 21 March 2018; Accepted: 17 May 2018; Published: 05 June 2018.

Reviewed by:

Copyright © 2018 Verhoeff, Knippels, Gilissen and Boersma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Roald P. Verhoeff, [email protected]

This article is part of the Research Topic

Systems Thinking in the K-12 Classroom: Perspective, Practice, and Possibilities

Hypothesis for Higher Education

Hypothesis empowers students and educators to highlight and comment on digital course materials, helping to develop reading comprehension and critical thinking skills, increase student engagement, and create community in online, hybrid, and in-person courses.

Social annotation works right on top of existing course content to:

• Develop foundational and advanced skills in reading, writing and critical thinking
• Build connections that support community within the class and across campus
• Encourage peer-to-peer learning and strengthen digital collaboration skills
• Provide instructors with early and ongoing insight into student engagement, comprehension and skill development

Explore our collection of conversations with teachers, example assignments and grading rubrics to get ideas about how to add social annotation to your courses.

Assignments

A video discussion about the power of social annotation to build community and critical thinking in a range of subjects. Jeremy Dean sits down with Silvia Muller, Educational Researcher and Instructor at Rutgers; Christie DeCarolis, Instructional Designer and an adjunct professor at Rutgers-Camden; and Rachel Derr, Director of Pre-licensure Programs and Clinical Assistant Professor at Rutgers University Camden School of Nursing.

A video discussion about from Mary Isbell of the University of New Haven and John Stewart of the University of Oklahoma, both of whom have long used social annotation to make reading active, visible, and social with their students, and see it as essential for knowledge sharing, community building, and student success.

A video discussion with guests Carmen Johnston from Chabot College and Denise Maduli-Williams from San Diego Miramar College to learn how they are using social annotation to engage students from “all walks of classrooms.”

• Annotation Starter Assignments: A series of general starter assignments for different points in the semester.
• Ongoing Assignment : This assignment imagines Hypothesis as a go-to reading and collaborating tool for an entire course.
• Social Annotation Assignment from Katherine D. Harris, Department of English and Comparative Literature at San Jose State University.
• Annotations Rubric : a descriptive, three-level rubric from Katherine D. Harris at San Jose State University.
• Social Annotation Marking Rubric and Checklist : a descriptive, four-level rubric from Vanier College
• Collaborative Annotations Rubric : a descriptive, five-level rubric from M. Emilia Barbosa and Rachel Schneider at Missouri University of Science and Technology

What teachers are saying

“Our use of Hypothesis has helped (and forced, some of them have told me, happily) students to read more carefully and more deeply. They are helping each other make sense of the readings before they even get to class, which means that the small amount of time for discussion is better spent on real analysis work, evaluating arguments, comparing texts. When we get to class they already have an idea of how others are thinking and what others’ questions are, and discussion ends up being an extension of those ongoing conversations.”

Jasmine Ma, NYU Steinhardt School of Culture, Education, and Human Development

“Hypothesis is a great way to celebrate the diversity of thought in a community college classroom. Social annotation can help expose the strengths of learners who havenʼt been told their interpretation of any text is valid, important, and central to helping others add meaning to their own sensemaking.”

Quill West, Pierce College

Example courses using Hypothesis

• English and Composition
• Foreign Languages
• Political Science
• First Year Experience

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See all schools using Hypothesis .

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Students’ Sense of Belonging Matters: Evidence from Three Studies

On Thursday, February 16, we hosted Dr. Maithreyi Gopalan to discuss her latest research on how students’ sense of belonging matters.

• Evidence has shown that in certain contexts, a student’s sense of belonging improves academic outcomes, increases continuing enrollment, and is protective for mental health. In some of the studies presented, these correlations were still present beyond the time frame of the analysis, suggesting that belonging might have a longitudinal effect.
• Providing a more adaptive interpretation of challenge seemed to help students in a belonging intervention make alternative and more adaptive attributions for their struggles, forestalling a potential negative impact on their sense of belonging.

Professor Gopalan began her talk by discussing how the need for “a sense of belonging” has been identified as a universal and fundamental human motivation in the field of psychology. John Bowlby, one of the first to conduct formal scientific research on belonging, examined the effects on children who had been separated from their parents during WWII (Baumeister & Leary, 1995). From his pioneering work, Bowlby and colleagues proposed that humans are driven to form lasting and meaningful interpersonal relationships, and the inability to meet this need results in loneliness and mental distress. Educational psychologists adapted the concept of belonging to indicate how students’ sense of fit with themselves and with their academic context can affect how they perceive whether they can thrive within it (Eccles & Midgley, 1989; Eccles & Roeser, 2011).

After providing this brief overview of what belonging means more broadly, Dr. Gopalan introduced the concept of “belonging uncertainty” pioneered by social psychologists Geoffrey Cohen and Gregory Walton at Stanford University (Walton & Cohen, 2007) to describe the uncertainty students might feel about their belonging when entering a new social and academic situation , which is most pronounced during times of transition (e.g., entering college). Research has shown that belonging uncertainty affects how students make sense of daily adversities, often interpreting negative events as evidence for why they do not belong. Belonging uncertainty may result in disengagement and poor academic outcomes. In contrast, a sense of belonging is associated with academic achievement, persistence in the course, major, and college (Walton & Cohen, 20011, Yeager & Walton, 2011). It is the concept of belonging uncertainty that is the focus of Dr. Gopalan’s presentation, with emphasis on the findings from the following key research questions:

• How do students’ sense of belonging in the first year correlate with academic persistence and outcomes at a national level?
• Can belonging interventions during the first semester of college lead to increased persistence and academic achievement in a diverse educational setting?
• How does a student’s sense of belonging amidst the COVID-19 pandemic correlate with mental health?

Study 1: College Students’ Sense of Belonging: A National Perspective (Gopalan & Brady, 2019)

Most research examining college students’ sense of belonging has come from studies looking at one or a few single four-year institutions. To examine how belonging differs across student identities and institutions, Professor Gopalan and colleagues looked at the responses from the only nationally representative survey of college students to date that had measured belonging. The Beginning Postsecondary Students Longitudinal Study (BPS) (Dudley et al ., 2020) sampled first-time beginning college students from 4070 eligible two- and four-year institutions (N= 23, 750 students), surveyed during their first year and subsequently two years later.

Professor Gopalan examined average measurements of belonging across institution type and student characteristics (Gopalan & Brady, 2019) and associations between belonging measurements and measurements of academic achievement, including GPA and persistence (continued enrollment), self-reported mental health, and self-reported use of campus services. The results, Dr. Gopalan explained, were striking: underrepresented racial and ethnic minority students (URMs) and first-generation/low-income students (FGLIs) reported a lower sense of belonging in four-year colleges than their non-URM and non-FGLI counterparts. 1 Importantly, they also found that having a greater sense of belonging is associated with higher academic performance, persistence, and is protective for mental health in year three of students’ undergraduate trajectory, suggesting that belonging might have a longitudinal effect (Gopalan & Brady, 2019). These findings were consistent with previous results from smaller studies involving single institutions. Sense of belonging is important not just in specific institutions but nationally, and social identity and context matter . One practical and policy-driven takeaway from this study is that only one national data set currently measures students’ sense of belonging using a single item. More robust measurements and large data sets might reveal additional insights into the importance of belonging for students’ educational experiences.

1 At two-year colleges, first-year belonging is not associated with persistence, engagement, or mental health. This suggests that belonging may function differently in two-year settings. More work is ongoing to try to understand the context that might be driving the difference. (Deil-Amen, 2011).

Study 2: A customized belonging intervention improves retention of socially disadvantaged students at a broad-access university (Murphy et al ., 2020)

Professor Gopalan and colleagues wanted to understand how to adapt existing belonging interventions to different educational contexts and dig deeper into underlying psychological processes underpinning belonging uncertainty. Because previous social-belonging interventions were conducted in well-resourced private or public institutions, Professor Gopalan was interested in examining whether the positive effects of belonging interventions could be extended to a broader-access context (context matters as not all extensions of belonging interventions have been shown to reproduce persistent changes in enrollment and academic outcomes). For this purpose, the traditional belonging interventions were customized for a four-year, Hispanic-serving public university with an 85% commuter enrollment using focus groups and surveys. Based on prior research, belonging interventions provide an adaptive lay theory for why students encounter challenges during transition times (Yeager et al ., 2016). Students, particularly those with little knowledge of how college works or those who have experienced discrimination, or are aware of negative stereotypes about their social group, may make global interpretations of why college can be challenging and may even associate challenges as evidence that they and students like them don’t belong. With belonging interventions, the lay theory provided to students aims to frame the experience of challenge in more adaptive ways—challenge and adversity are typical experiences, particularly during transitional moments, and should be expected; adapting academically and socially takes time—students will be more likely to persist, seek out campus resources and develop social relationships.

• They acknowledge that challenges are expected during transitions and that these are varied.
• They communicate to students that most students, including students from non-minority groups, experience similar challenges and feelings about them.
• They communicate that belonging is a process that takes time and tends to increase over time
• They use student examples of challenges and resolutions.

The Intervention

All students in the first-year writing class were randomly assigned to either the belonging group or an active control group. The intervention was provided to first-year students in their writing class and consisted of a reading and writing assignment about social and academic belonging. The control group was given the same assignment but with a different topic, study skills. In the intervention group, students read several stories from a racially diverse set of upper-level students who reflected on the challenges of making friends and adjusting to a new academic context. The hypothetical students reflected on the strategies they used, the resources they accessed, and how the challenge dissipated over time. After the reading exercise, the students in the intervention group were instructed to write about how the readings echoed their own first-year experiences. Then, they were asked to write a letter to future students who might question their belonging during their transition to college. Research has shown that written reflections help students internalize the main messages of the belonging intervention (Yeager & Walton, 2011).

Similar to previously published belonging interventions, results in persistence and academic achievement were significant for minoritized groups in the belonging cohort:

• Persistence. Compared to the control group, continuous enrollment for URM & FGLI students increased by 10% one year after and 9% two years after the intervention.
• Performance. The non-cumulative GPA from the URM & FGLI students increased by 0.19 points the semester immediately following the intervention and by 0.11 over the next two years compared to students in the control group.

Immediately following the intervention, a selected sub-sample of students in both conditions was invited to take a daily diary survey for nine consecutive days. The daily diary survey assessed students’ daily positive and negative academic and social experiences (students were asked to report and describe three negative and three positive events that they faced daily and to rate how positive and negative the events were), as well as their daily sense of social and academic belonging. The daily-diary assignment revealed another interesting finding: the intervention did not change the overall perception of negative events. URM & FGLI students in both groups had a statistically similar daily-adversity index and reported the same number of daily adverse events on average. However, there was no connection between the adversity index and sense of belonging for students in the belonging cohort. In contrast, students in the control group evidenced a negative correlation between daily adversities and belonging: “the greater adversity disadvantaged students experienced on a day, the lower their sense of social and academic fit” (Murphy et al ., 2020).

Providing a more adaptive interpretation of challenge seemed to help students in the belonging condition make alternative and more adaptive attributions for their struggles that did not connect to their sense of belonging. A follow-up survey one year after the intervention showed that minoritized students in the belonging intervention continued to report a higher sense of belonging in comparison to their counterparts in the control group.

Study 3: College Student’s Sense of Belonging and Mental Health Amidst the COVID-19 Pandemic (Gopalan et al ., 2022)

Dr. Gopalan presented the third study, which turned out to provide a unique opportunity to assess whether sense of belonging had predictive effects on mental health. In the fall of 2019, researchers sent a survey to students at a large, multicampus Northeastern public university called the College Relationship and Experience survey (CORE), which included two questions about belonging, among other items. In the Spring of 2020, after students were sent home due to the COVID-19 pandemic, a variation of the same survey was sent to students who had taken the CORE survey. After controlling for pre-COVID depression and anxiety, Dr. Gopolan and colleagues found that students who reported a higher sense of belonging in the fall of 2019 had lower rates of depression and anxiety midst-COVID pandemic , with the effects on depression more strongly predictive than those for anxiety. The correlation between a lower sense of belonging and higher rates of depression and anxiety was also found to be strongest for first-year students, who had little time during their first year to build community and adjust to college before the pandemic hit.

Dr. Gopalan concluded with some practical advice for instructors: “Stop telling students they belong, show them instead that they belong,” citing a recent op-ed from Greg Walton . We do this by modeling the idea that belonging is a process that takes time and by communicating to students that they are not alone , which can be done through sharing our own experiences with belonging, and by allowing students space to hear the experiences of their peers and learn from one another.

• Classroom Practices Library which includes Overview: Effective Social Belonging Messages are more.
• The Project for Education Research That Scales (PERTS) : a free belonging intervention for four-year colleges and universities.
• Research library on belonging
• Article on Structures for Belonging: A Synthesis of Research on Belonging-Supportive Learning Environments
• “Stop telling students ‘You Belong!’”
• Everyone is talking about belonging: What does it really mean?
• Post-secondary
• Academic Belonging : introduction to the concept and practices that support it.
• Flipping Failure : a campus-wide initiative to help students feel less alone by hearing stories about how their peers coped with academic challenges

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117 (3), 497–529. https://doi.org/10.1037/0033-2909.117.3.497

Deil-Amen, R. (2011). Socio-academic integrative moments: Rethinking academic and social integration among two-year college students in career-related programs. The Journal of Higher Education , 82(1), 54-91. https://doi.org/10.1080/00221546.2011.11779085  

Dudley, K., Caperton, S.A., and Smith Ritchie, N. (2020). 2012 Beginning Postsecondary Students Longitudinal Study (BPS:12) Student Records Collection Research Data File Documentation (NCES 2021-524). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved 2/27/2023 from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid-2021524

Eccles, J. S., & Midgley, C. (1989). Stage/Environment Fit: Developmentally Appropriate Classrooms for Early Adolescence. In R. E. Ames, & Ames, C. (Eds.), Research on Motivation in Education , 3, 139-186. New York: Academic Press.

Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21 (1), 225–241. https://doi.org/10.1111/j.1532-7795.2010.00725.x

Gopalan, M., & Brady, S. T. (2020). College Students’ Sense of Belonging: A National Perspective. Educational Researcher , 49(2), 134–137. https://doi.org/10.3102/0013189X19897622

Gopalan, M., Linden-Carmichael, A. Lanza, S. (2022). College Students’ Sense of Belonging and Mental Health Amidst the COVID-19 Pandemic, Journal of Adolescent Health , 70(2), 228-233. https://doi.org/10.1016/j.jadohealth.2021.10.010

Murphy, M.C., Gopalan, M., Carter, E. R., Emerson, K. T. U., Bottoms, B. L., and Walton, G.M., (2020). A customized belonging intervention improves retention of socially disadvantaged students at a broad-access university Science Advances, 6(29). DOI: 10.1126/sciadv.aba4677

Walton, & Cohen. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82. https://doi.org/10.1037/0022-3514.92.1.82

Walton, G.M., & Cohen, G.L. (2011). A Brief Social-Belonging Intervention Improves Academic and Health Outcomes of Minority Students. Science,  331(6023), 1447-1451.  DOI: 10.1126/science.1198364

Yeager, D. S., & Walton, G. M. (2011). Social-Psychological Interventions in Education They’re Not Magic. Review of Educational Research, 81(2), 267–301. http://doi.org/10.3102/0034654311405999

Yeager, D.S., Walton G.M., Brady, S.T., Dweck, C.S.,(2016). Teaching a lay theory before college narrows achievement gaps at scale, Psychological and Cognitive Sciences , 113(24), E3341-E3348. https://doi.org/10.1073/pnas.1524360113

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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From crisis to choice: A modern history of education reform in America

A review of ‘the parent revolution’ by corey deangelis.

Editor’s Note: Corey DeAngelis, Ph.D., will be giving a speech and signing books at a Mackinac Center event on Tuesday, May 21.  

Most authors choose to dedicate their book to their spouse, their kids or at least their agent. But Corey DeAngelis dedicates his book, “ The Parent Revolution: Rescuing Your Kids from the Radicals Ruining Our Schools ,” to American Federation of Teachers President Randi Weingarten and government teachers unions more broadly. DeAngelis, “public enemy #1 of the teachers unions,” writes of them:  “You’re doing more to advance freedom in education than anyone could have ever imagined. Thank you for overplaying your hand, showing your true colors, and sparking the Parent Revolution. ”

 The book mentions some distant history as well as some longstanding problems in the public educational system. But most of it focuses on the COVID-19 pandemic, the response from unions and their allies (in schools and legislatures), and the resulting backlash from parents.  

 The COVID-19 pandemic and officials’ responses to it were disasters for kids. Studies show dramatic drops in learning across the board, but especially for low-income students. Taxpayers got ripped off too. Spending on public schools skyrocketed, with little evidence it helped mitigate the spread of the virus or helped students recover academically.  

 The pandemic, however, may be the best thing that has ever happened to public education in America, according to DeAngelis. Why? Because it blew open the Overton Window for education policy and led to a dramatic increase in school choice. 

 Test scores plummeted in public schools during the pandemic, and schools have incurred many other problems. Private schools, however, have not been afflicted to nearly the same degree. Being responsive to parents, they were far more likely to weigh the risks and trade-offs of closing classrooms during the pandemic. They stayed open as much as possible.   

Why were public schools less responsive to parents? Many schools, DeAngelis argues, were not beholden to the students in the system, to their parents, or to taxpayers. Their chief concern rather was for the adults who run things — teachers unions and their elected political allies at the state and local level.  

 The evidence is immense, and DeAngelis does a good job showing his work. As his fans might say, “He has the receipts.” Among the worst:   

The Chicago Teachers Union leaders vacationed in Puerto Rico while fighting to keep the district closed. (This came after they tweeted that “the push to reopen schools is rooted in sexism, racism and misogyny.”)

AFT President Weingarten oversaw local unions that repeatedly fought to keep schools closed, but during congressional testimony in 2023, she claimed, “We spent every day … trying to get schools open.” (The reality was that the Centers for Disease Control and Prevention often took its lead from the union in urging schools to stay shut to in-person learning).  

Pennsylvania Gov. Tom Wolfe not only ordered all public and private schools to close during the pandemic, but he also closed online charter schools that served 37,000 students. Why? To “protect public schools from competition,” DeAngelis says.   

Districts and regions with stronger teachers unions stayed closed longer than those elsewhere. But even more politically conservative states got run over by unions and their allies. Arizona, North Carolina and Virginia shut their doors for education while, ironically, being open for day-care services.  

“Parent Revolution” gives a bit of a history lesson on education, but its emphasis is on the era from the start of the pandemic until now. Advocates for school choice had secured small wins over the decades, but the pandemic and the years since then greatly increased educational options.  

States’ and schools’ responses to the pandemic highlighted the extreme positions of teachers unions, state and federal bureaucrats and many school administrators. Parents saw firsthand how the public school system is often run in the interest of adults, rather than kids. This realization united a groundswell of opposition, in conservative and liberal areas alike. Major policy changes resulted, at both the local and state policy level.  

Three years after the start of the pandemic, in 2023, school choice programs in 20 states expanded. Fourteen states now have nearly universal school choice programs. In these states, nearly all parents can get financial support, such as a voucher or tax credit, to pick from a variety of private and public school options.  

 DeAngelis thinks the opposition to school choice is confusing and hypocritical. Opponents of choice make a great fuss about allowing students to take a voucher or a tax credit and spend it at any school option they want. DeAngelis points out that this has been allowed, without controversy, in many other parallel situations: Pell grants and the GI Bill; Head Start and state-funded pre-K programs; food stamps and housing subsidies; and a host of other public programs. In all of these, the government picks up the tab but allows the beneficiary to spend the money with private entities.  

 So, what drives the opposition to school choice for K-12 education? The same thing, DeAngelis supposes, that drove the opposition to schools re-opening: the system has long been built to benefit adults rather than kids. These adults are backed by public sector unions, who fund the campaigns of those officials who then pass the rules. The unions’ efforts often result in higher pay, more benefits, and contracts favorable to themselves, not taxpayers or children.

DeAngelis grew up attending public schools, as I did. Like me, he had a parent who worked in public schools. Like me, he had a mixed experience. And also like me, he came to learn that the United States runs its education system in a nonsensical fashion. So we both favor reforms to that system. He writes, “I came to the conclusion that in America, nowhere was the problem of monopoly power more pronounced — and more harmful to our society — than the nation’s government-run school system.”  

My wife and I received a public school education, from kindergarten through to high school graduation. Our school-age children are in local public schools. My father and mother, sister and brother all work or worked in the public school system. Still, I agree with DeAngelis and support school choice. Families and individual children are very different from each other, and they need a variety of options that fit their needs. The pandemic showed the full extent of the problem. Competition and choice will prevent it from happening again.  

Permission to reprint this blog post in whole or in part is hereby granted, provided that the author (or authors) and the Mackinac Center for Public Policy are properly cited.

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An Examination of Educational Leadership Preparation in Ontario: Are Principals Prepared to Lead Equitably?

• Nia Spooner University of Toronto

Author Biography

Nia spooner, university of toronto.

Nia Dara Spooner is a doctoral candidate in Educational Leadership and Policy at the Ontario Institute for Studies in Education, University of Toronto. She has extensive experience teaching in cross-cultural contexts, which has informed her scholarly interests. Nia’s research focuses on culturally responsive and equity-oriented leadership in education. 

Abawi, Z. (2021). Race(ing) to the top: Interrogating the underrepresentation of BIPOC education leaders in Ontario public schools. Journal of Higher Education Policy and Leadership Studies, 2(1), 80–92. https://doi.org/10.52547/johepal.2.1.80

Barr, J., & Saltmarsh, S. (2014). “It all comes down to the leadership”: The role of the school principal in fostering parent-school engagement. Educational Management Administration & Leadership, 42(4), 491–505. https://doi.org/10.1177/1741143213502189

Brown, A., & Danaher, P. A. (2019). CHE Principles: Facilitating authentic and dialogical semi-structured interviews in educational research. International Journal of Research & Method in Education, 42(1), 76–90. https://doi.org/10.1080/1743727x.2017.1379987

Capper, C. A., Alston, J., Gause, C. P., Koschoreck, J. W., López, G., Lugg, C. A., & Mckenzie, K. B. (2006). Integrating lesbian/gay/bisexual/transgender topics and their intersections with other areas of difference into the leadership preparation curriculum: Practical ideas and strategies. Journal of School Leadership, 16(2), 142–157. https://doi.org/10.1177/105268460601600203

Cherkowski, S. (2010). Leadership for diversity, inclusion and sustainability: Teachers as leaders. Citizenship, Social and Economics Education, 9(1), 23–31. https://doi.org/10.2304/csee.2010.9.1.23

Churchley, J., Handford, V., Neufeld, P., & Purvey, D. (2017). Mapping principal preparation programs in British Columbia, Canada. International Journal of Humanities and Education, 3(2), 110–128. https://dergipark.org.tr/en/pub/ijhe/issue/35953/404060

Creswell, J. W. (2012). Education research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.

Davis, S. H., & Darling-Hammond, L. (2012). Innovative principal preparation programs: What works and how we know. Planning and Changing, 43(1/2), 25–45.

Dei, G. J. S. (2011). Defense of official multiculturalism and recognition of the necessity of critical anti-racism. Canadian Issues, 15–19.

Education Act, R.S.O. 1990, c. E.2, Ontario Regulation 274/12: Hiring Practices (2012). https://www.ontario.ca/laws/regulation/120274/v1

Evans, A. E. (2007). School leaders and their sensemaking about race and demographic change. Educational Administration Quarterly, 43(2), 159–188. https://doi.org/10.1177/0013161x06294575

Fields, X., & Wotipka, C. M. (2022). Effect of LGBT anti-discrimination laws on school climate

and outcomes for lesbian, gay, and bisexual high school students. Journal of LGBT Youth, 19(3), 307–329. https://doi.org/10.1080/19361653.2020.1821276

Galloway, M. K., & Ishimaru, A. M. (2017). Equitable leadership on the ground: Converging on

high-leverage practices. Education Policy Analysis Archives, 25(2), 1–33. https://doi.org/10.14507/epaa.25.2205

George, R. C., Maier, R., & Robson, K. (2020). Ignoring race: A comparative analysis of education policy in British Columbia and Ontario. Race Ethnicity and Education, 23(2), 159–179. https://doi.org/10.1080/13613324.2019.1679754

Guillaume, R. O., Saiz, M. S., & Amador, A. G. (2020). Prepared to lead: Educational leadership graduates as catalysts for social justice praxis. Journal of Research on Leadership Education, 15(4), 283–302. https://doi.org/10.1177/1942775119829887

Howard, T. C. (2003). Culturally relevant pedagogy: Ingredients for critical teacher reflection. Theory Into Practice, 42(3), 195–202. https://doi.org/10.1207/s15430421tip4203_5

Jiang, B., Patterson, J., Chandler, M., & Chan, T. C. (2009). Practicum experience in educational leadership program: Perspectives of supervisors, mentors and candidates. Educational Administration: Theory and Practice, 15(57), 77–108. https://dergipark.org.tr/en/pub/kuey/issue/10340/126688

Kempf, A. (2020). If we are going to talk about implicit race bias, we need to talk about

structural racism: Moving beyond ubiquity and inevitability in teaching and learning about race. Taboo: The Journal of Culture and Education, 19(2), Article 10. https://digitalscholarship.unlv.edu/taboo/vol19/iss2/10

Khalifa, M. (2018). Culturally responsive school leadership. Harvard Education Press.

Kutsyuruba, B., Klinger, D. A., & Hussain, A. (2015). Relationships among school climate,

school safety, and student achievement and well-being: A review of the literature. Review

of Education, 3(2), 103–135. https://doi.org/10.1002/rev3.3043

Ladson‐Billings, G. (1995). But that’s just good teaching! The case for culturally relevant pedagogy. Theory Into Practice, 34(3), 159–165. https://doi.org/10.1080/00405849509543675

Ladson-Billings, G. & Tate, W. F. (1995). Toward a critical race theory of education. Teachers

College Record, 97(1). 47–68.

Ledesma, M. C., & Calderón, D. (2015). Critical Race Theory in education: A review of past literature and a look to the future. Qualitative Inquiry, 21(3), 206–222. https://doi.org/10.1177/1077800414557825

Lopez, A. E. (2015). Navigating cultural borders in diverse contexts: Building capacity through culturally responsive leadership and critical praxis. Multicultural Education Review, 7(3), 171–184. https://doi.org/10.1080/2005615x.2015.1072080

Lopez, A. E. (2019). Anti-Black racism in education: School leaders’ journey of resistance

and hope. In R. Papa (Ed.), Handbook on promoting social justice in education (pp. 1–16). Springer. https://doi.org/10.1007/978-3-319-74078-2_37-1

Love, B. L. (2019). We want to do more than survive: Abolitionist teaching and the pursuit

of educational freedom. Beacon Press.

McMahon, B. (2007). Educational administrators’ conceptions of whiteness, anti‐racism and social justice. Journal of Educational Administration, 45(6), 684–696. https://doi.org/10.1108/09578230710829874

Ontario College of Teachers. (2017). Principal’s Qualification Program guideline. https://www.oct.ca/-/media/pdf/principals%20qualification%20program%202017/2017%20pqp%20guideline%20en%20web_accessible.pdf

Parsons, J., & Beauchamp, L. (2012). Leadership in effective elementary schools: A synthesis of five case studies. US-China Education Review B, 8, 697–711.

Pollock, K., & Briscoe, P. (2019). School principals’ understandings of student difference and diversity and how these understandings influence their work. International Journal of Educational Management, 34(3), 518–534. https://doi.org/10.1108/ijem-07-2019-0243

Rogers-Ard, R. & Knaus, C. B. (2020). But I don’t want to be a principal: Intersectional

racism and black leadership pathways. In Black Educational Leadership. Routledge.

Safir, S., & Dugan, J. (2021). Street data: A next-generation model for equity, pedagogy, and school transformation. Corwin Press.

Saldaña, J. (2013). The coding manual for qualitative researchers (2nd ed). SAGE Publications.

Santamaría, L. J. (2021). Applied Critical Leadership: A way forward through shared

disproportionate trauma. Academia Letters, Article 592. https://doi.org/10.20935/al592

Santamaría, L. J., & Jean-Marie, G. (2014). Cross-cultural dimensions of applied, critical, and transformational leadership: Women principals advancing social justice and educational equity. Cambridge Journal of Education, 44(3), 333–360. http://dx.doi.org/10.1080/0305764x.2014.904276

Santamaría, L. J., & Santamaría, A. P. (2012). Applied critical leadership in education: Choosing change. Routledge. https://doi.org/10.4324/9780203818688

Santamaría, L. J., & Santamaría, A. P. (2015). Counteracting educational injustice with applied critical leadership: Culturally responsive practices promoting sustainable change. International Journal of Multicultural Education, 17(1), 22–41. https://doi.org/10.18251/ijme.v17i1.1013

Santamaría, L. J., Santamaría, A. P., & Singh, G. K. P. (2017). One against the grain: Re-imagining the face of school leadership in Aotearoa New Zealand. International Journal of Educational Management, 31(5), 612–621. http://dx.doi.org/10.1108/ijem-11-2016-0237

Santamaría, L. J., Santamaría, A. P., Webber, M., & Pearson, H. (2014). Indigenous urban school leadership (IUSL): A critical cross-cultural comparative analysis of educational leaders in New Zealand and the United States. Canadian and International Education, 43(1), Article 5. https://doi.org/10.5206/cie-eci.v43i1.9240

Stone-Johnson, C., Gray, C., & Wright, C. (2021). Learning to lead socially just schools: An exploration of candidate readiness for equity-focused principal preparation in the United States. Professional Development in Education, 47(1), 36–49. https://doi.org/10.1080/19415257.2020.1814386

Tate, W. F. (1997). Chapter 4: Critical race theory and education: History, theory, and implications. Review of Research in Education, 22(1), 195–247. https://doi.org/10.3102/0091732x022001195

Taylor, C. & Peter, T., with McMinn, T. L., Elliott, T., Beldom, S., Ferry, A., Gross, Z., Paquin, S., & Schachter, K. (2011). Every class in every school: Final report on the first national climate survey on homophobia, biphobia, and transphobia in Canadian schools. Egale Canada Human Rights Trust. https://egale.ca/wp-content/uploads/2011/05/Every-Class-In-Every-School-Final-Report.pdf

Taylor, K., Coulombe, S., Coleman, T. A., Cameron, R., Davis, C., Wilson, C. L., Woodford, M.

R., & Travers, R. (2022). Social support, discrimination, and self-esteem in LGBTQ + high school and post-secondary students. Journal of LGBT Youth, 19(3), 350–374. https://doi.org/10.1080/19361653.2020.1812465

The Canadian Press. (2023, August 23). N.B. doubles down on LGBTQ school policy after report

says it violates Charter rights. CTV News. https://atlantic.ctvnews.ca/n-b-doubles-down-on-lgbtq-school-policy-after-report-says-it-violates-charter-rights-1.6531389

The Institute for Education Leadership. (2013). The Ontario Leadership Framework: A school and system leader’s guide to putting Ontario’s Leadership Framework into action. https://www.education-leadership-ontario.ca/application/files/8814/9452/4183/Ontario_Leadership_Framework_OLF.pdf

Theoharis, G., & Haddix, M. (2011). Undermining racism and a whiteness ideology: White

principals living a commitment to equitable and excellent schools. Urban Education, 46(6), 1332–1351. https://doi.org/10.1177/0042085911416012

Tooms, A. (2007). The right kind of queer: Fit and the politics of school leadership. Journal

of School Leadership, 17(5), 601–630. https://doi.org/10.1177/105268460701700503

Tuck, E., & Yang, K. W. (2012). Decolonization is not a metaphor. Decolonization: Indigeneity,

Education & Society, 1(1), 1–40. https://jps.library.utoronto.ca/index.php/des/article/view/18630

Tuters, S., & Portelli, J. (2017). Ontario school principals and diversity: Are they prepared to

lead for equity? International Journal of Educational Management, 31(5), 598–611.

https://doi.org/10.1108/ijem-10-2016-0228

Wallin, D. C., & Peden, S. (2014). Touring Turtle Island: Fostering leadership capacity to

support First Nations, Métis, and Inuit learners. In Education, 19(3), 47–68. https://doi.org/10.37119/ojs2014.v19i3.150

Webber, C. F., Mentz, K., Scott, S., Okoko, J. M., & Scott, D. (2014). Principal preparation in Kenya, South Africa, and Canada. Journal of Organizational Change Management, 27(3), 499–519. https://doi.org/10.1108/jocm-07-2013-0125

Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91. https://doi.org/10.1080/1361332052000341006

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The Toyota Corolla Theory of College

O ver recent decades, the price of higher-education tuition has risen faster than costs in any other major consumer category , outpacing even medical care and housing. Despite that, for a while, applications kept flooding in—and for good reason: College was still worth it. As the MIT economist David Autor argued in 2014, “the real lifetime earnings premium to college education has likely never been higher.”

Yet today, the value of college is slipping in Americans’ eyes. Fifty-six percent of respondents to a recent survey said a four-year degree was a “bad bet.” Enrollment has been declining since its 2010 peak —a change explained partly by the shrinking pool of 18-year-olds but also by high-school students simply opting out. From 2018 to 2021, the proportion of high-school graduates entering college fell by 7 percentage points—a decline driven by falling enrollment in two-year programs in particular.

These numbers reflect only one dimension of the various threats now facing higher education. Academic departments across the country are hollowed out and underfunded; the personal finances of graduate students and non-tenure-track faculty members are precarious; and students are fearful of losing their high-stakes financial wager on a degree.

[ Ben Sasse: How to really fix higher ed ]

One common response from industry observers and policy makers is to urge institutions to focus exclusively on the value students are getting for the money they’re spending: placing a priority on vocational and STEM education (preferably delivered online and at scale) while paring back amenities, student services , administration , and supposedly unremunerative humanities coursework. After all, the argument runs, if colleges refuse to rid themselves of their excesses, they may find that someone else will do it for them. The Bloomberg business columnist Adrian Wooldridge recently compared the United States’ higher-education sector to “the country’s car industry in the 1970s, just before it was taken apart by the Japanese—hampered by a giant bureaucracy, contemptuous of many of its workers, and congenitally inward-looking.”

The comparison works because Detroit’s mid-century offerings tended to be oversize, overloaded with features, and overpriced. By contrast, Japanese imports—Toyotas, in particular—were smaller, cheaper, and more fuel-efficient. When oil prices rose in the 1970s and the value proposition of large cars grew dicey, Japanese automakers were able to grab market share from American manufacturers. For U.S. colleges to avoid such decline, the thinking goes, schools must strip down to create an affordable and job-ready product.

The impulse to cut education to the bone is not new. A century ago, a wave of school reform swept the country, propelled by the psychologist Edward L. Thorndike’s reductionist theory of learning. Thorndike believed that learning was a solitary act readily quantifiable through testing, which was also, he and his allies believed, a sound measure of students’ innate intelligence.

Thorndike’s sometime-colleague and competitor, John Dewey, disagreed. Dewey argued that learning was a social, experimental process—too complex to be usefully pared down to its constituent parts. At his Laboratory School at the University of Chicago, students worked together on interdisciplinary projects, motivated, according to his alternate theory, not by some distant reward but by curiosity about the subject.

By the 1910s and ’20s—the heyday of Taylorist scientific management —Dewey’s romantic, unquantifiable vision of learning didn’t stand a chance . A generation of reformers committed to efficiency and standardization used tools such as credit hours, common grading systems, and uniform curricula to square off schools’ idiosyncratic edges.

Students, too, underwent standardization. Thorndike believed that native intelligence was fixed and unimprovable, and so an important function of school was to winnow out supposedly undeserving students. “The one thing that the schools or any other educational forces can do least,” he wrote in 1903, was increase students’ “powers and capacities.” Lamentably, he connected this notion with race, arguing for separate vocational and technical training programs for Black Americans.

By the 1920s, administrative mandates were coming to dominate classrooms—even if the lessons were still run by teachers, in all their variety. Thorndike had dreamed of someday eliminating them. “If, by a miracle of mechanical ingenuity,” he mused in 1912, “a book could be so arranged that only to him who had done what was directed on page one would page two become visible, and so on, much that now requires personal instruction could be managed by print.”

Such a book is no longer so hard to imagine. Artificial intelligence could plausibly remove that last bastion of personal, “soft” influence from education. This possibility is alarming to those of us who believe in student-teacher relationships—particularly because, in an ironic twist, AI’s existence may place a premium on hard-to-automate problem-solving and interpersonal skills. Even if AI-powered instruction doesn’t displace existing colleges and universities, it might still create a tiered system: one with teachers, another without.

Already, educational standardization has brought unintended consequences. Especially harmful is the pervasive idea that learning—the fundamental objective of school—must serve double duty as the means to constantly sort adept students from those assumed to be inept. As we described in our 2020 book, Grasp: The Science Transforming How We Learn , a cognitive price must be paid when courses are optimized to compare students with one another. Such sorting gets in the way of context-rich, curiosity-fueled learning that leads to lasting knowledge and potent skills.

In fact, if today’s higher-education sector and 1960s-era Detroit resemble each other, that’s because the same destructive degree of standardization found on the auto assembly line has been applied in teaching. Detroit’s workers performed simple, repetitive tasks, and when defects appeared, they weren’t empowered to act on the issue—only to flag it. This supposedly efficient practice produced tremendous waste: expensive repairs on completed cars, piles of defective parts, and a legacy of mechanical problems for motorists.

Toyota, by contrast, trusted its small teams of workers to stop the production line and work backward to root out the cause of any defects. Suppliers built new parts only upon request, which meant fewer faulty items. Instead of requiring individuals to perform a task over and over, Toyota assigned several jobs to its teams while seeking their insights into how to improve production. To the delight of Americans who took a chance on them, the resulting cars proved remarkably reliable. By 1987, the company’s Takaoka plant was producing cars at twice the speed of General Motors’s plant in Framingham, Massachusetts, with a defect rate a third of GM’s.

[ Read: Why is college in America so expensive? ]

Wooldridge’s point that U.S. higher education should be more like 1970s Toyota is well taken—but not because Toyota outdid Detroit in eliminating human complexity. Rather, it showed that the greatest efficiency can be achieved by putting that pesky human element to good use.

Higher-education institutions should consider pursuing a similar strategy : investing in students and teachers while stripping away obstacles in their path. Online instruction can be used to reduce costs, for instance, but instead of simply shunting classes onto Zoom, schools should confine lectures to prerecorded video and open up valuable class time for in-person work and discussion. Co-op programs that send students into the workforce can help them develop job-ready knowledge and industry relationships. Curricular programs that mingle STEM and humanities courses can establish a mixture of hard and soft skills that will have lasting value by enriching context and cultivating curiosity. More esoteric institutional measures—such as breaking the bachelor’s degree into portable “micro-credentials”—could provide flexibility for the students who have lately decided against even a two-year degree.

At the beginning of the 1960s, Americans could buy just two types of cars: a mass-produced one, which was affordable for most but of inferior quality, or a hand-built one, which made superior quality available to only a wealthy few. Japanese manufacturers soon debunked this trade-off, showing that efficiency and quality could coexist. In higher education, a thoughtless approach to efficiency could entrench the pre-Toyota dynamic, leading to a small set of elite schools offering a human-centered education while everyone else makes do with an algorithmic junk version. We don’t have to limit ourselves to such a choice. We can have the best of both worlds—if we have the audacity to build it.

Is new AP African American Studies course too woke? We attended class to find out.

A usa today analysis reveals what kinds of school districts are offering the ap african american studies course this year – and which ones aren't..

LORTON, Virginia – Sean Miller quiets the stereo blasting Al Green’s “Let’s Stay Together” and begins his lessons for the day, a journey through centuries with improvisational stops along the way.

He kicks off with a discussion on Black joy . Today’s focus: Olympic gymnast and fellow Virginian Gabby Douglas . Then his roughly two dozen students move on to another topic: Black History Month . Has the yearly event outlived its relevance? Miller asks after playing a video clip about the month’s origins. No, some say. A teen proposes legislation to mandate its observance.

Next the discussion veers to distinctions between “slave” and “enslaved person." One emphasizes a person's status, the other their humanity. Then students analyze sketches of captives from the 1839 Amistad rebellion. By the time class wraps up, Miller has drifted through the good and bad referenced in the Al Green anthem, stringing together topics like a chord progression. "Finding the triumph amid all the challenges and tragedies requires a little bit of creativity," he later explains.

Welcome to Advanced Placement African American Studies. The course – still in pilot mode – has drawn praise from students nationwide but sparked restrictions in Florida and Arkansas amid concerns from conservatives that the curriculum is leftist propaganda and makes white children feel bad about themselves.  

The course has the rigor of a college-level offering and the interdisciplinary scope of an ethnic studies seminar, comprising four units that extend from ancient African civilizations to modern-day movements. In mid-May, about 13,000 students at 700 schools in 42 states and Washington, D.C., will be eligible to take the AP African American Studies test. High scores could earn students credit at more than 300 colleges that have indicated they'll grant it.

While the stakes and the difficulty level are high, students say the material is resonant and accessible. Caury Crusoe, 17, said the class often feels like “an hour-and-a-half conversation.” In interviews with USA TODAY, students and educators described the course as transformational. Taking it improved their self-esteem and gave them a newfound pride in their ancestors, many Black teens said. Others emphasized the illuminating content and their deeper appreciation for what humans have in common versus what they don’t. 

The immense demand from teens – especially Black youth,  who participate in AP classes at lower rates  than their white and Asian peers – suggests many more U.S. schools will pick up the course once it goes live this fall. The AP class could continue to face headwinds in the coming years as proposed bans targeting critical race theory (CRT) and diversity, equity and inclusion (DEI) turn up on legislative agendas.

The College Board declined to say which schools are offering the class. But states, districts, universities and local reporters helped USA TODAY identify roughly 370 campuses based in nearly 200 school districts that are offering the class, accounting for more than half the schools piloting it. Some of these campuses, including the only Florida institution piloting it, are private schools.

A large majority of the schools and districts offering AP African American Studies are in communities that voted for President Joe Biden in 2020, USA TODAY found. Taken together, those districts also had a greater percentage of Black high school students than the national average.

A separate USA TODAY analysis of email correspondence from education officials in some red states revealed staffers’ hesitancy to embrace the course because of the optics.

“There’s always a certain amount of fear and anxiety and backlash that’s associated with these efforts,” said Michael Hines, a Stanford education historian who studies activism in African American communities. Hines foresees an ongoing battle over this course as part of a “recurring cycle.”

AP African American Studies sparks concerns over critical race theory

The launch of AP African American Studies is a watershed moment that says "African American history – the African diaspora – it matters,” said Thomas Tucker, the chief equity officer with Kentucky’s education department. “My hope is that historians will look back on this period to say we’re finally at a point of helping Americans ... understand the beautiful complexity, the beautiful tapestry, of the long, long history of the African people.”

Critics often portray AP African American Studies as a course fixated on division and suffering. That perception nearly deterred two of Miller’s students from signing up. Renee Prox, 17, wondered whether college admissions officers would look down on her for taking it because of the politics. “I wasn’t sure if it would look bad on my transcript,” said the senior, who is Black. 

One of Prox’s few white classmates, Abigail Plageman, also debated whether to take the course. Plageman said her parents were skeptical because of what they’d heard in the news about CRT.

Claims the course would delve into CRT, a graduate-level theory that examines how racism permeates societies and systems, were widespread as the framework underwent revisions.

Florida's education department in January 2023 banned the course because it lacked "educational value," and Gov. Ron DeSantis described a draft framework as a "political agenda" that sought to "shoehorn" radical progressive concepts into history instruction. A subsequent version excluded many themes DeSantis called out , prompting critiques from course advocates who accused the College Board of whitewashing content educators had extensively workshopped. Experts last summer convened for another round of edits that led to a version released in December . Some controversial topics – like intersectionality – were reintroduced, while others – like the Black Lives Matter movement – remain optional . 

After DeSantis banned the course, Arkansas’s education department restricted it, too, and several red states promised to review it. (Ultimately, half a dozen Arkansas campuses opted to pilot it , but, because of the state's stance, participating students cannot earn credit toward graduation. However, now that the framework has been updated, Arkansas students may be able to earn credit next year under the state's graduation requirements, a department spokesperson told USA TODAY.)

Explained: Gov. Ron DeSantis' feud with the College Board over AP African American Studies

Email correspondence obtained by USA TODAY shows some employees in red states last year were questioning what to do about the class following DeSantis’s and others’ critiques. 

“I am a bit concerned about this course,” wrote Davonne Eldredge, North Dakota’s assistant director of academic support, in a January 2023 email to a superior about a College Board request that the state adopt it. “This is the course that has been in the national news due to critical race theory concerns brought forth in Florida. … Given the hot item critical race is within ND, I’m not sure how to proceed with this one. My gut says to hold off until the changes are made.” The state never formally reviewed the course because no school asked to pilot it, a spokesperson said.

In Virginia, another state where officials vowed to review the curriculum, media coverage of the controversy seemingly prompted decision-makers to waffle over their messaging. The state education department concluded that the course complied with Gov. Glenn Youngkin’s anti-CRT executive order and drafted a related statement to share its conclusion with the public, email records show. The draft included a suggestion that more edits be made to the course, but staffers worried that language would prompt inquiries from reporters.

South County High, where Miller teaches, is one of about 16 schools in Virginia piloting the class this school year. In that state, AP African American Studies will remain an elective rather than a social studies course that counts toward graduation.

Despite the revisions, some critics who closely follow the course’s development remain concerned.

Michael Gonzalez, a fellow at the conservative Heritage Foundation, said CRT and the activist mindset of the Black Lives Matter movement remain prominent in the latest framework even though these topics have been left out or made optional. As long as discussions of systemic racism are included, it’s still grounded in CRT, in his view. The word “oppression,” he stressed, is mentioned 19 times. 

In an interview with USA TODAY, Gonzalez and his colleague, Jonathan Butcher, agreed it's important to teach about the horrors of slavery and Jim Crow. But viewing all history through the lens of racism and oppression, they said, is misleading and incendiary.

“We want young people to believe that the American dream belongs to them … that they do have a future,” said Butcher, an education policy research fellow at Heritage. “If you , instead , give young people something that they need to resist, to look down upon, you’re robbing them of the chance of having something to live up to.”

Seeing Black history ‘for more than just the bad’

Over several months and during two visits to Miller's classroom , students told USA TODAY the course broadens their knowledge and instills hope. Plageman, the white student whose parents were skeptical about the course, said she’s politically centrist and feels the lessons haven’t changed her viewpoints. Rather, they’ve expanded what she knows about the U.S. AP African American Studies is not CRT, she said, but “just another social studies class that’s different from what I’ve learned before.”

Prox, the Black student concerned about having the course on her transcript, explained she hasn’t looked back since she signed up. “It’s way more than what it’s been painted out to be,” she said. “It’s about how the African diaspora has grown and how we started and it’s just a really good representation of Black people.”

“We were oblivious to how the story of an African past is a glorious one. It’s not just a reshaping of the narrative. It is an introduction of a narrative that, for so many of us, simply did not exist." Teresa Reed, a dean and music professor at the University of Louisville, who serves on the course’s development committee

Black history is under attack: From AP African American Studies to ‘Ruby Bridges’

Crusoe, who described the course as a long conversation, said the knowledge she’s gained has made her more ambitious and proud. It inspired her to branch beyond her goal of studying business to explore a humanities discipline with an emphasis on social justice when she attends North Carolina A&T, a historically Black college in Greensboro, next fall. She regularly shares tidbits from class with her mom and is president of her school's Black Student Alliance. “I’m not a big history person, but (Miller) makes me want to talk about it,” she said. 

Her favorite part, beyond Miller’s relaxed teaching style, has been learning about the strength of her ancestors. About the ancient African civilizations with legacies far more expansive than she ever knew. About the modern Black heroes whose art touches millions. About the everyday Black people, like her parents and herself, fulfilling the American dream. “People just don’t really see Black history for more than just the bad,” she said. 

The Heritage scholars said a better strategy for teaching about African American experiences would be to broaden U.S. history education. That way, the history of Black Americans could be woven together with other groups. “African American history is my history,” Gonzalez said. 

Students and educators told USA TODAY there’s a reason to separate this curriculum: U.S. history classes seldom scrape past the surface of African Americans’ role in the narrative, beyond slavery and civil rights.

It’s groundbreaking for an African American studies course like this to come together on a national scale, endorsed by the College Board. “It feels like a gap is being filled in my own psyche,” said Teresa Reed, a dean and music professor at the University of Louisville who serves on the course’s development committee .

Growing up in predominantly Black Gary, Indiana, in the 1960s and 70s, Reed didn’t learn much about the contributions of her ancestors or the pre-slavery chapters of their story.

“We were oblivious to how the story of an African past is a glorious one,” she said. That’s what makes this course so novel: “It’s not just a reshaping of the narrative. It is an introduction of a narrative that, for so many of us, simply did not exist."

‘These are people who persevered’

The interdisciplinary, meandering nature of the South County High classes USA TODAY observed this spring is precisely what developers envisioned when they designed the course. The idea is for students to find connections between the past and present – between the Amistad rebellion and Gabby Douglas setting Olympic records.

Miller, a former marketing professional who chairs the high school’s social studies department, infuses his lessons with guidance on developing real-world skills. During a March class, students broke into groups to plan a podcast project. The final product, Miller told them, should be a neatly structured, professional-quality episode that touches on several topics of their choice from the curriculum. Their grade would count as their test score for the unit. 

The classroom buzzed as the teens deliberated how to tackle this assignment. Crusoe and her partner designed logos and debated which to use – an animated ear against the word “Black” repeated in rows or a woman in 1950s attire with a TV as a head entitled “Shades of History”? Across the room, a boy scanned C-SPAN for good clips. In the back, three students discussed themes they’d highlight: Black music, fashion, literature and media representation.  

Miller, who wears gauges in his ears and produces music in his spare time, peppers his lessons with a mix of banter, sarcasm and big words. Students fist-bump him on their way in and out. His mantra is simple: “Keep the rigor high, keep the expectations high. But create memories and keep it engaging.” Slavery, lynching and segregation are crucial elements of the African American experience, but Miller frames the discussion around resilience rather than bitterness or shame.

He strives to frame his instruction around “victories over victimization,” he said that March day. Wearing a "Built by Black History" T-shirt, he noted, “There were some dark days, but there were also some very positive days in response.”

“I want them to walk out the door and say, ‘Wow, these are people who persevered.'” 

Contributing: Doug Caruso, data editor at USA TODAY, analyzed demographic and political trends across districts we identified were piloting AP African American Studies this school year.  

Also contributing: Lily Altavena, Detroit Free Press; Caroline Beck, Indianapolis Star; Jillian Ellison, Journal & Courier; Samantha Hernandez, Des Moines Register; Kelly Lyell, the Coloradoan; Madeleine Parrish, Arizona Republic .

J.D. Tuccille: Rampant DEI nonsense to blame for the chaos seizing higher education

Universities have succeeded in radicalizing their campuses. Eliminating mandatory ideological pledges to DEI is the first step to recovery

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With American college campuses convulsed by protests that are anti-Israeli, anti-American and often antisemitic, it’s a good time to ask how students at the country’s institutions of higher education are so profoundly radicalized and hostile to their own society. The answer is that, to a great extent, these schools are the authors of their own problems. They worked hard to politicize students and, at least with some, succeeded.

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New York University social psychologist Jonathan Haidt, co-author of The Coddling of the American Mind ,  notes that campus bureaucracies around freshman orientation and student life are constantly growing — and they’re staffed by people who are even further to the political left than professors.

“They’re largely getting PhDs from education schools,” Haidt explained. “They’re very ideological. And their goal is to shape incoming students to be warriors for social justice as they see it.”

This shaping comes in the form of mandatory training nominally intended to help students interact with classmates of different backgrounds, races, sexual orientations and what have you. But the training, often presented under the banner of diversity, equity and inclusion (DEI), is deeply politicized and leads receptive subjects towards what Haidt characterizes as a “hate-filled, binary, us-versus-them worldview.”

Inculcation of this worldview is pervasive on college campuses. One report published last month by Speech First, an American free speech advocacy organization, examined 248 colleges and universities in the country and found that a “significant majority (67 per cent) of these institutions mandate DEI academic courses to satisfy general education requirements.”

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Such courses pop up everywhere. Arizona’s Goldwater Institute reported in March  that journalism students at Arizona State University’s Walter Cronkite School of Journalism and Mass Communication are required to take a course that pushes identity politics, lectures students on so-called “microaggressions” and preaches the doctrine of “privilege” that some people supposedly acquire simply by being born.

“If you identify with the gender you were assigned at birth, here are a bunch of unearned benefits you get that many folks do not,” students are told in a section that then launches into a litany of such perks.

Arizona is better than some places given that the state’s board of regents barred public universities from requiring DEI statements — pledges of allegiance to a specific point of view — of job applicants.

The Foundation for Individual Rights and Expression (FIRE) warned about these pledges in 2022 : “Vague or ideologically motivated DEI statement policies can too easily function as litmus tests for adherence to prevailing ideological views on DEI, penalize faculty for holding dissenting opinions on matters of public concern and ‘cast a pall of orthodoxy’ over the campus.”

In April, Kansas adopted FIRE’s model legislation forbidding public colleges to “condition admission or educational aid to an applicant for admission, hiring an applicant for employment or hiring, reappointing or promoting a faculty member, on the applicant’s or faculty member’s pledging allegiance to or making a statement of personal support for or opposition to any political ideology or movement.”

The broad language of the bill is important to forestall an end-run by DEI advocates through shifting terminology. It’s also necessary to prevent advocates of other ideologies from adopting loyalty-oath tactics in the future. Infusing educational institutions with rigid orthodoxy is wrong for all movements, not just for the totalitarian problem child of the moment.

But that legislation doesn’t address the incorporation of DEI into curriculums, which is a concern for Speech First. In its report, the organization proposes to “prohibit the mandatory inclusion of ideological activism courses, such as critical race theory and DEI, as a condition for obtaining a degree” and “ensure that universities provide instruction in foundational principles of the United States that make up our legal system and governing structures.”

But a requirement that universities provide instruction in any sort of principles, no matter how good, is not compatible with prohibiting ideological activism. To the contrary, it sounds a lot like a mission to replace one flavour of ideological instruction with another. Speech First might prefer a different set of ideas, but those should no more be foisted on captive students than should DEI.

An additional problem is that prohibiting ideologically charged courses is going to be a challenge. Faculty capable of crafting mandatory DEI courses at a journalism school are perfectly capable of renaming their classes something innocuous and then playing Whac-a-Mole with the enforcers of neutrality.

At a time when surveys find that left-leaning faculty outnumber those leaning to the right at American universities five-to-one, and the ratio among administrators is a mind-blowing 12-to-one, policing ideological content in classes is a losing proposition. Undoing damage to higher education that took years to develop will also take a long time.

Ending universities’ self-adopted role of crafting students into radical social justice warriors will require remaking those schools. Professors and administrators will have to be more likely to disagree with each other so that they’re not delivering unquestionable orthodoxies to students. Then students can be educated in a healthy atmosphere of competing views rather than received wisdom.

Writing in 2018 of his experience as a Columbia University undergraduate with one especially doctrinaire instructor, Coleman Hughes recalled, “Voicing a strong pushback against any idea that the professor favoured was nearly unthinkable. ”

Eliminating DEI statements — or any other ideological loyalty oaths — as conditions of admission, employment or graduation is the first step down a long path to restoring open inquiry to schools.

That’s the one good thing about the current moment’s campus chaos. It’s a wake-up call to universities that became accustomed to smugly grooming students as radical activists and are now reaping what they sowed. If they don’t respond to that call, we can do so ourselves — by choosing education elsewhere.

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