vocabulary about scientific problem solving

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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8th Grade Science/Section 2: Scientific Problem Solving

• 1 As You Read
• 2 Scientific Methods
• 3.1 Observe
• 4 Form a Hypothesis
• 5.1 Plan the Experiment
• 5.2 Do the Experiment
• 6 Analyze Your Data
• 7 Draw Conclusions and Communicate

As You Read [ edit | edit source ]

• Explain the steps taken in scientific methods.
• Compare and contrast scientific variables and constants.
• Explain how a control is used during an experiment.
• Vocabulary: scientific methods, observation, inference, hypothesis, independent variable, dependent variable, constant, and control.

Scientific Methods [ edit | edit source ]

Several steps were taken to learn about the pottery found at York Middle School. When the pottery was found, a decision was made to stop construction at the site. One adult guessed that the pottery was old. An expert was called to verify the guess made about the pottery. Based on prior knowledge and further testing, it was concluded that the pottery was from a prehistoric culture.

Think about the last time you had a problem that took several steps or actions to solve. Step-by-step procedures of scientific problem solving are called scientific methods . Solving any problem scientifically involves several steps. The steps used can vary from situation to situation and aren’t always done in the same order. But for now, take a look at each step in turn.

Recognize the Problem [ edit | edit source ]

Ben thought about all the science he had learned over the past few months. He was eager to find out more about the world around him. As he looked around his bedroom, he wondered what he could explore. It was then that Ben noticed that the plant on his windowsill was droopy. He quickly watered the wilting plant. Later in the day, Ben observed that the droopy plant had perked up. He concluded that he should water the plant on a regular basis. Every day after school, he watered the plant in his room.

After a few weeks, Ben noticed that the leaves on his plant had turned yellow and brown. He knew from science class that plants need water, so why was this plant not doing well. He talked to his teacher about the plant. She suggested that Ben use what he learned in science class to solve his problem. She pointed out that this problem might make a good project for the upcoming science fair.

Ben already has completed the first step in using a scientific approach to solving a problem - he recognizes a problem. A scientific problem is simply a question you don’t know the answer to. To solve his problem, Ben must do research about his plant. Using sources of information, Ben identified his plant as a fig. In his Science Journal, he drew a picture of the plant and listed some facts about it.

Observe [ edit | edit source ]

In order for Ben to be able to answer the question about why his plant was not thriving, he needed to plan and carry out an experiment. First, he made and recorded careful observations about his plant. Observations can be bits of information you gather with your senses. Most scientific observations are made with your eyes and ears. You also can observe with your senses of touch, taste, and smell. Ben observed that many of the leaves had fallen off his plant. The stem, in places, was peeling. Ben also noticed that some white, powdery, smelly stuff was covering the soil in the pot. He stuck his finger into the soil. It was wet.

Infer [ edit | edit source ]

Observations like Ben’s often lead to inferences. An inference is a conclusion about an observation. Ben inferred that perhaps he was watering his plant too often. Can you make any other inferences about why Ben’s plant wasn’t thriving?

Form a Hypothesis [ edit | edit source ]

After a problem is identified, a scientist might make a hypothesis. A hypothesis is a statement that can be tested. Hypotheses are based on observations, research, and prior knowledge of a problem. Sometimes more than one hypothesis can be developed.

Example 1 compares and contrasts hypotheses with two other scientific statements - scientific theories and scientific laws. Ben decided to use his inference about watering too often as his hypothesis. His hypothesis was the following: Fig plants grow best when they are watered only once a week.

• Hypothesis: A hypothesis is a statement that is tested with experiments. Hypotheses supported by repeated tests are used to form theories.
• Theory: A theory is an explanation supported by results of many experiments. Theories attempt to explain why something happens.

Test Your Hypothesis [ edit | edit source ]

To test his hypothesis, Ben will carry out an experiment using three plants. An experimental investigation is a series of carefully planned steps used to test a hypothesis. In any experiment, it’s important to carefully consider what resources you will use and how you can take steps to prevent wasting those resources. It’s also important to keep everything the same except for the item or variable you are testing so that you’ll know which variable caused the results. The one factor that you change in an experiment is called the independent variable .

In Ben’s proposed experiment, the independent variable is the number of times he waters each plant in a week. He then will observe how well each plant grows based on the amount of water it receives. The growth of the plants is the dependent variable in Ben’s experiment. A dependent variable is the factor, or outcome, that will be measured in an experiment.

Plan the Experiment [ edit | edit source ]

In order to test only one variable at a time, scientists often use constants. Constants are factors in an experiment that stay the same. In his experiment, Ben will use the same species and size of plants, which will be potted with the same kinds and amounts of soil. His teacher pointed out that Ben also must put his plants into identical containers. Another constant in Ben’s experiment will be the amount of light each plant will get.

Experimental investigations also have a control. A control is a standard used for comparison. Ben knows that all plants, even cacti, need water. His control will be that one of the plants receives no water during the experiment.

Do the Experiment [ edit | edit source ]

Ben gathered all the materials he would need to test his hypothesis. Before he started, Ben knew from Ms. Garcia’s labs that he must write down a plan to follow. In his Science Journal, he wrote that he would use three fig plants. Plant A would not be watered. This would be his control. A second fig plant, Plant B, would get watered every day. The third fig plant, Plant C, would get watered only once each week. His experiment would last one month.

Ben then made a table in which to record his observations. He listed each plant and the number of times it was to get watered. Ben made room in the table for his measurements. He also made a plan to record his observations, which would include the height of each plant, the color of its leaves, and the number of leaves it dropped, if any.

Analyze Your Data [ edit | edit source ]

Data are collected during any scientific study. Some data are numeric values such as the length of an object or the temperature of a liquid. Other data you collect may include observations that use adjectives and phrases such as “faster”, “smaller”, “not as well as”, and “greener”. An experimenter must record and study the data collected before he or she can draw conclusions about an experiment.

By the end of the month, Ben observed that the leaves still left on the plant that received no water were brown and shriveled. It had lost most of its leaves. The plant that was watered every day had a few leaves left on its branches, but these leaves didn’t look too healthy. A white, smelly substance covered the soil. Ben noticed that the plant that was watered once each week had grown the tallest. Many healthy green-and-white leaves hung from its branches.

Draw Conclusions and Communicate [ edit | edit source ]

After studying his data, Ben was ready to draw some conclusions. A conclusion is a statement based on what was observed. Ben concluded that not watering a plant causes the leaves to dry out and die. Watering a plant too much also causes the leaves to die. Watering the plant once a week seems to be the best schedule, of those tested, for a fig plant.

Ben told his teacher about his results. She reminded him that in order to make sure his conclusions were valid, he should repeat his experiment. Ben agreed and did the same experiment again. Based on the results of his second experiment, Ben was able to conclude confidently that watering a fig plant once a week made it grow well in the conditions he used. His hypothesis was supported. An important step in the scientific process is to communicate the results of an investigation. Ben entered his project in his school’s science fair. He seemed to have won the science fair with his project and his teacher congratulated him and he thanked her for her help.

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• 1 - Building a Scientific Vocabulary
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Academic Word List - General Terms

The following terms are part of the academic language of science, yet are not specific to science. It is necessary to understand these terms if one is to read and understand science literature. For more information on academic language, click here .

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Two approaches to teaching young children science concepts, vocabulary, and scientific problem-solving skills

2012, Early Childhood Research Quarterly

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How to Train Your Problem-Solving Skills

From the hiccups that disrupt your morning routines to the hurdles that define your professional paths, there is always a problem to be solved. 

The good news is that every obstacle is an opportunity to develop problem-solving skills and become the best version of yourself. That’s right: It turns out you can get better at problem-solving, which will help you increase success in daily life and long-term goals.  

Read on to learn how to improve your problem-solving abilities through scientific research and practical strategies.

Understanding Problem-Solving Skills

You may be surprised to learn that your problem-solving skills go beyond just trying to find a solution. Problem-solving skills involve cognitive abilities such as analytical thinking, creativity, decision-making, logical reasoning, and memory. 

Strong problem-solving skills boost critical thinking, spark creativity, and hone decision-making abilities. For you or anyone looking to improve their mental fitness , these skills are necessary for career advancement, personal growth, and positive interpersonal relationships. 

Core Components of Problem-Solving Skills Training

To effectively train your problem-solving skills, it’s important to practice all of the steps required to solve the problem. Think of it this way: Before attempting to solve a problem, your brain has already been hard at work evaluating the situation and picking the best action plan. After you’ve worked hard preparing, you’ll need to implement your plan and assess the outcome by following these steps:  

• Identify and define problems: Recognizing and clearly articulating issues is the foundational step in solving them.
• Generate solutions: Employing brainstorming techniques helps you develop multiple potential solutions.
• Evaluate and select solutions: Using specific criteria to assess solutions helps you choose the most effective one.
• Implement solutions: Developing and executing action plans, including preparing for potential obstacles, guides you to positive outcomes.
• Review and learn from outcomes: Assessing the success of solutions and learning from the results for future improvement facilitates future success. 

Strategies for Developing Problem-Solving Skills

There are many practical exercises and activities that can improve problem-solving abilities.

Cultivate a Problem-Solving Mindset

• Adopt a growth mindset: A growth mindset involves transforming phrases like “I can’t” into “I can’t yet.” Believing in the capacity to improve your skills through effort and perseverance can lead to greater success in problem-solving.
• Practice mindfulness: Mindfulness can enhance cognitive flexibility , allowing you to view problems from multiple perspectives and find creative solutions.

Enhance Core Cognitive Skills 

• Strengthen your memory: Engage in activities that challenge your memory since accurately recalling information is crucial in problem-solving. Techniques such as mnemonic devices or memory palaces can be particularly effective.
• Build your critical thinking: Regularly question assumptions, evaluate arguments, and engage in activities that require reasoning, such as strategy games or debates.

Apply Structured Problem-Solving Techniques

• Use the STOP method: This stands for Stop , Think , Observe , and Plan . It's a simple yet effective way to approach any problem methodically, ensuring you consider all aspects before taking action.
• Try reverse engineering: Start with the desired outcome and work backward to understand the steps needed to achieve that result. This approach can be particularly useful for complex problems with unclear starting points.

Incorporate Technology into Your Training

• Engage with online courses and workshops: Many platforms offer courses specifically designed to enhance problem-solving skills, ranging from critical thinking to creative problem-solving techniques.
• Use cognitive training apps: Apps like Elevate provide targeted, research-backed games and workouts to improve cognitive skills including attention, processing speed, and more. 

Practice with Real-World Applications and Learn from Experience

• Tackle daily challenges: Use everyday issues as opportunities to practice problem-solving. Whether figuring out a new recipe or managing a tight budget, applying your skills in real-world situations can reinforce learning.
• Keep a problem-solving journal: Record the challenges you face, the strategies you employ, and the outcomes you achieve. Reflecting on your problem-solving process over time can provide insights into your strengths and areas for improvement.

Embracing Problem-Solving as a Lifelong Journey

Since problems arise daily, it’s important to feel confident in solving them. 

And you can do just that by downloading the Elevate brain training app. Elevate offers 40+ games and activities designed to improve problem-solving, communication, and other cognitive skills in a personalized way that’s backed by science. Pretty cool, right? 

Consider downloading the Elevate app on Android or iOS now—it’ll be the easiest problem you solve all day. 

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Avi Wigderson wins $1 million Turing Award for using randomness to change computer science

The 2023 Turing Award has been given to Avi Wigderson . The mathematician found that adding randomness into algorithms made them better at solving nondeterministic problems.

The 2023 Turing Award has been given to Avi Wigderson , a mathematician who discovered the strange connection between computation and randomness. 

Wigderson was announced the winner of the Association for Computing Machinery (ACM) A.M. Turing Award, often called the " Nobel Prize of Computing," on April 10, 2024.

The award, given with a prize of $1 million, comes just three years after Wigderson, a professor of mathematics at the Institute for Advanced Study in Princeton, New Jersey, won the 2021 Abel Award for his contributions to computer science. Wigderson's theoretical work has been key to the development of numerous advances in computing , from cloud networks to cryptography methods that underpin cryptocurrencies.

"Wigderson is a towering intellectual force in theoretical computer science, an exciting discipline that attracts some of the most promising young researchers to work on the most difficult challenges," Yannis Ioannidis , president of the ACM, said in a statement . "This year's Turing Award recognizes Wigderson's specific work on randomness, as well as the indirect but substantial impact he has had on the entire field of theoretical computer science."

Related: Scientists uncover hidden math that governs genetic mutations

Computer algorithms are deterministic by nature, which enables them to make predictions but also limits their grasp of the messy randomness found in the real world. In fact, many problems are considered computationally “hard”, and deterministic algorithms struggle to solve them efficiently.

— Newly discovered 'einstein' tile is a 13-sided shape that solves a decades-old math problem

— Centuries old 'impossible math problem cracked using physics of Schrödinger's cat

— Two mathematicians just solved a decades-old math riddle — and possibly the meaning of life

But Wigderson and his colleague Richard Karp , a computer scientist at the University of California, Berkeley, found a way to tame computational hardness. After inserting randomness into their algorithms, they found that they made some problems much easier to solve.

Sign up for the Live Science daily newsletter now

Get the world’s most fascinating discoveries delivered straight to your inbox.

Wigderson chased this observation, proving in later work that the reverse also applied: Randomness could always be stripped from probabilistic algorithms to transform them into deterministic ones. His findings illuminated the connection between computational hardness and randomness in ways that reshaped computer science.

"From the earliest days of computer science, researchers have recognized that incorporating randomness was a way to design faster algorithms for a wide range of applications," Jeff Dean , chief scientist at Google Research and Google DeepMind, said in the statement. "Efforts to better understand randomness continue to yield important benefits to our field, and Wigderson has opened new horizons in this area."

Ben Turner is a U.K. based staff writer at Live Science. He covers physics and astronomy, among other topics like tech and climate change. He graduated from University College London with a degree in particle physics before training as a journalist. When he's not writing, Ben enjoys reading literature, playing the guitar and embarrassing himself with chess.

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To Fight Climate Change, We Need New ‘Political Technologies’

By Peter Coy

Opinion Writer

Science alone won’t stop the planet from overheating. But science coupled with political science just might.

That’s the theme of a new book, “Long Problems: Climate Change and the Challenge of Governing Across Time.” It’s by Thomas Hale, an American political scientist who teaches at the University of Oxford’s Blavatnik School of Government.

Hale argues that people are too quick to throw up their hands because the political will to stop climate change is lacking. For political scientists, he writes, “this is not the end but rather the start of the intellectual challenge.”

Hale has specific ideas for how to change institutions and procedures so that today’s inhabitants of Earth give more consideration to tomorrow’s inhabitants. He calls them, at one point, “political technologies,” a phrase I like.

Long problems such as climate change are ones in which there is a long lag between causes and effects. They are hard to solve, especially with today’s institutions. We don’t act early because we’re uncertain about how big the problem is, and it isn’t as salient as the daily emergencies all around us. Our hesitation gives an opening to obstructionist forces. Today’s decision makers vow to protect the planet for future generations, but the unborn multitudes are mere “shadows” to them, as Hale puts it.

On top of all that, Hale writes, “Institutions created to address the early phase of a long problem struggle to remain useful as the problem’s structure develops over time.” Case in point: The United Nations Framework Convention on Climate Change, which was created in 1992. The original concept was for countries to make binding commitments to fight climate change. As the organization has evolved, though, “nothing is agreed until every country agrees on every point,” Hale writes.

That’s not useful. A better approach is the Paris Agreement of 2015, which went into effect the next year. It allows countries to set their own targets for greenhouse gas reductions while triggering a “norm cascade” that induces them to do more and more. Hale likes the Paris Agreement on the whole, though he says it’s not perfect.

Society has already invented institutions and systems that bring future considerations to the fore, Hale writes. The Congressional Budget Office and similar offices in other countries analyze how new legislation will affect economic growth and government finances in the long run. The bond market assesses whether bond issuers, such as governments, will be able to pay back what they owe. Insurance companies — which Hale doesn’t mention — won’t issue policies unless customers take steps to reduce their risks.

On climate, too, there have been efforts to create institutions and processes that help solve Hale’s long problems. Some governments are requiring business to incorporate the “social cost” of carbon into their decisions. And the Intergovernmental Panel on Climate Change brings together the world’s top experts and issues closely followed reports.

There are many more opportunities for political engineering, Hale writes. He approvingly mentions the Finnish Parliament’s cross-party Committee for the Future and the Finnish Government Report on the Future, which interact. He recommends more experimentation in policymaking — as Chinese leaders put it, crossing the river by feeling for stones.

To get the public and lawmakers thinking more about the future, he endorses Britain’s Climate Change Committee, which he writes “has become a significant political force for the long-term interest,” and similar organizations (some of them not as effective) in Hungary, Israel, Malta, Sweden, Tunisia and the United Arab Emirates.

To insulate long problems from partisan politicking, he recommends the appointment of a trustee to oversee climate decisions, analogous to the way a politically insulated central bank is delegated the authority to conduct monetary policy. The California Air Resources Board is “perhaps the strongest, though still imperfect” example of such an institution in the realm of the environment, he writes. (Hale told me he’s not aware of anything quite like the California agency elsewhere in the world.)

“Long Problems” is a kind of nonfiction counterpart to Kim Stanley Robinson’s science fiction book from 2020, “The Ministry for the Future,” which took seriously the idea that future generations need to be given as much consideration as our own.

Hale is a co-leader of the Net Zero Tracker , which tracks the decarbonization progress of countries and companies, and the Net Zero Regulation and Policy Hub . He told me that he has been involved in helping people at the United Nations prepare for a Summit for the Future, which will be held Sept. 22-23. On the U.N. website is an early draft of a declaration to be issued at the summit, which says among other things that “our conduct today will impact future generations exponentially.”

Anne-Marie Slaughter, the chief executive of the think tank New America who was Hale’s adviser on his doctorate at Princeton, shared a byline with Hale and two of his Oxford colleagues on a policy brief , “Toward a Declaration on Future Generations,” that recommends the U.N. appoint “a special envoy or high commissioner” to be a voice for the future.

I kind of prefer “envoy” because it sounds like the person has literally come from the future.

No one solution will instantly end the political obstacles to fighting climate change. Some of the ideas in Hale’s book may not pan out at all. But I give him credit for focusing on how to solve problems in which the cause and the effect are separated by decades. Getting the “political technology” right is every bit as important as inventing better solar cells, wind turbines and batteries.

Outlook: Oliver Allen

Retail sales rose more than expected in March, but the “booming” pace of growth isn’t likely to last, Oliver Allen, a senior U.S. economist at Pantheon Macroeconomics, wrote in a client note on Monday. “It is hard to see how the strength in consumption can continue for much longer, now that real after-tax income growth has slowed markedly, the bulk of excess savings from earlier in the pandemic has been spent, and a raft of leading indicators point to a marked softening in the labor market,” Allen wrote.

Quote of the Day

“Why do we need to make the rich richer to make them work harder but make the poor poorer for the same purpose?”

— Ha-Joon Chang, “Economics: The User’s Guide” (2014)

Peter Coy is a writer for the Opinion section of The Times, covering economics and business. Email him at [email protected] . @ petercoy

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Computer Science > Computer Vision and Pattern Recognition

Title: pay attention to your neighbours: training-free open-vocabulary semantic segmentation.

Abstract: Despite the significant progress in deep learning for dense visual recognition problems, such as semantic segmentation, traditional methods are constrained by fixed class sets. Meanwhile, vision-language foundation models, such as CLIP, have showcased remarkable effectiveness in numerous zero-shot image-level tasks, owing to their robust generalizability. Recently, a body of work has investigated utilizing these models in open-vocabulary semantic segmentation (OVSS). However, existing approaches often rely on impractical supervised pre-training or access to additional pre-trained networks. In this work, we propose a strong baseline for training-free OVSS, termed Neighbour-Aware CLIP (NACLIP), representing a straightforward adaptation of CLIP tailored for this scenario. Our method enforces localization of patches in the self-attention of CLIP's vision transformer which, despite being crucial for dense prediction tasks, has been overlooked in the OVSS literature. By incorporating design choices favouring segmentation, our approach significantly improves performance without requiring additional data, auxiliary pre-trained networks, or extensive hyperparameter tuning, making it highly practical for real-world applications. Experiments are performed on 8 popular semantic segmentation benchmarks, yielding state-of-the-art performance on most scenarios. Our code is publicly available at this https URL .

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