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The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information

Micah altman.

1 Center for Research in Equitable and Open Scholarship, MIT Libraries, Massachusetts Institute of Technology, Cambridge, MA, United States

Philip N. Cohen

2 Department of Sociology, University of Maryland, College Park, MD, United States

The scholarly knowledge ecosystem presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale. This article draws upon significant reports on aspects of the ecosystem to characterize the most important research challenges and promising potential approaches. The focus of this review article is the fundamental scientific research challenges related to developing a better understanding of the scholarly knowledge ecosystem. Across a range of disciplines, we identify reports that are conceived broadly, published recently, and written collectively. We extract the critical research questions, summarize these using quantitative text analysis, and use this quantitative analysis to inform a qualitative synthesis. Three broad themes emerge from this analysis: the need for multi-sectoral cooperation and coordination, for mixed methods analysis at multiple levels, and interdisciplinary collaboration. Further, we draw attention to an emerging consensus that scientific research in this area should by a set of core human values.

The Growing Importance of the Scientific Information Ecosystem

“The greatest obstacle to discovery is not ignorance—it is the illusion of knowledge.” —Daniel J. Boorstin

Over the last two decades, the creation, discovery, and use of digital information objects have become increasingly important to all sectors of society. And concerns over global scientific information production, discovery, and use reached a fever-pitch in the COVID-19 pandemic, as the life-and-death need to generate and consume scientific information on an emergency basis raised issues ranging from cost and access to credibility.

Both policymakers and the public at large are making increasingly urgent demands to understand, improve, and govern the large-scale technical and human systems that drive digital information. The scholarly knowledge ecosystem 1 presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale.

Scientific study of the scholarly knowledge ecosystem has been complicated by the fact that the topic is not the province of a specific field or discipline. Key research in this area is scattered across many fields and publication venues. This article integrates recent reports from multiple disciplines to characterize the most significant research problems—particularly grand challenges problems—that pose a barrier to the scientific understanding of the scholarly research ecosystem, and traces the contours of the approaches that are most broadly applicable across these grand challenges. 2

The remainder of the article proceeds as follows: Characterizing the Scholarly Knowledge Ecosystem section describes our bibliographic review approach and identifies the most significant reports summarizing the scholarly knowledge ecosystem. Embedding Research Values section summarizes the growing importance of scientific information and the emerging recognition of an imperative to align the design and function of scholarly knowledge production and dissemination with societal values. Scholarly Knowledge Ecosystem Research Challenges section characterizes—impact scientific research problems selected from these reports. Commonalities Across the Recommended Solution Approaches to Core Scientific Questions section identifies the common shared elements of solution approaches to these scientific research problems. Finally, Summary section summarizes and comments on the opportunities and strategies for library and information science researchers to engage in new research configurations.

Characterizing the Scholarly Knowledge Ecosystem

The present and future of research—and scholarly communications—is “more.” By some accounts, scientific publication output has doubled every 9 years, with one analysis stretching back to 1650 (Bornmann and Mutz, 2015 ). This growth has been accompanied by an increasing variety of scholarly outputs and dissemination channels, ranging from nanopublications to overlay journals to preprints to massive dynamic community databases. 3 As its volume has multiplied, we have also witnessed public controversies over the scholarly record and its application. These include intense scrutiny of climate change models (Björnberg et al., 2017 ), questions about the reliability of the entire field of forensic science (National Research Council, 2009 ), the recognition of social biases embedded in algorithms (Obermeyer et al., 2019 ; Sun et al., 2019 ), and the widespread replication failures across medical (Leek and Jager, 2017 ) and behavioral (Camerer et al., 2018 ) sciences.

The COVID-pandemic has recently provided a stress test for scholarly communication, exposing systemic issues of volume, speed, and reliability, as well as ethical concerns over access to research (Tavernier, 2020 ). In the face of the global crisis, the relatively slow pace of journal publication has spurred the publication of tens of thousands of preprints (Fraser et al., 2020 ), which in turn generated consternation over their veracity (Callaway, 2020 ) and the propriety of reporting on them in major news media (Tingley, 2020 ).

This controversy underscores calls from inside and outside the academy to reexamine, revamp, or entirely re-engineer the systems of scholarly knowledge creation, dissemination, and discovery. This challenge is critically important and fraught with unintended consequences. While calls for change reverberate with claims such as “taxpayer-funded research should be open,” “peer review is broken,” and “information wants to be free,” the realities of scholarly knowledge creation and access are complex. Moreover, the ecosystem is under unprecedented stress due to technological acceleration, the disruption of information economies, and the divisive politics around “objective” knowledge. Understanding large information ecosystems in general and the scientific information ecosystem in particular, presents profound research challenges with huge potential societal and intellectual impacts. These challenges are a natural subject of study for the field of information science. As it turns out, however, much of the relevant research on scholarly knowledge ecosystems is spread across a spectrum of other scientific, engineering, design, and policy communities outside the field of information.

We aimed to present a review that is useful for researchers in the field of information in developing and refining research agendas and as a summary for regulators and funders of areas where research is most needed. To this end, we sought publications that met the following three criteria:

• ° Characterizing a broad set of theoretical, engineering, and design questions relevant to how people, systems, and environments create, access, use, curate, and sustain scholarly knowledge.
• ° Covering multiple research topics within scholarly knowledge ecosystems.
• ° Synthesizing multiple independent research findings.
• ° Indicative of current trends in scholarship and scholarly communications.
• ° Published within the last 5 years, with substantial coverage of recent research and events.
• ° Reflecting the viewpoint of a broad set of scholars.
• ° Created, sponsored, or endorsed by major research funders or scholarly societies.
• ° Or published in a highly visible peer-reviewed outlet.

To construct this review, we conducted systematic bibliographic searches across scholarly indices and preprint archives. This search was supplemented by forward- and backward- citation analysis of highly cited articles; and a systematic review of reports from disciplinary and academic societies. We then filtered publications to operationalize the selection goals described above. This selection process yielded the set of eight reports, listed in Table 1 .

Key reports relevant to the scholarly knowledge ecosystem.

Collectively the reports in Table 1 integrate perspectives from scores of experts, based on examination of over one thousand research publications and scholarship from over a dozen fields. In total, these reports span the primary research questions associated with understanding, governing, and reengineering the scholarly knowledge ecosystem.

To aid in identifying commonalities across these reports, we coded each report to identify important research questions, broad research areas (generally labeled as opportunities or challenges), and statements declaring core values or principles needed to guide research. We then constructed a database by extracting the statements, de-duplicating them (within work), standardizing formatting, and annotating them for context. 4 Table 2 summarizes the number of unique coded statements in each category by type and work.

Extent of coded content.

Embedding Research Values

Science and scholarship have played a critical role in the dramatic changes in the human condition over the last three centuries. The scientific information ecosystem and its governance are now recognized as essential to how well science works and for whom. Without rehearsing a case for the value of science itself, we observe that the realization of such value is dependent on a system of scholarly knowledge communication.

In recent years we have seen that the system for disseminating scholarly communications (including evaluation, production, and distribution) is itself a massive undertaking, involving some of the most powerful economic and political actors in modern society. The values, implicit and explicit, embodied in that system of science practice and communication are vital to both the quality and quantity of its impact. If managing science information is essential to the potential positive effects of science, then the values that govern that ecosystem are essential building blocks toward that end. The reports illustrate how these values emerge through a counter-discourse, the contours of which are visible across fields.

All of the reports underscored 5 the importance of critical values and principles for successful governance of the scholarly ecosystem and for the goals and conduct of scientific research itself. 6 These values overlapped but were neither identical in labeling nor substance, as illustrated in Table 3 .

Core values and principles identified in each report.

Although the reports each tended to articulate core values using somewhat different terminology, many of these terms referred to the same general normative concepts. To characterize the similarities and differences across reports, we applied the 12-part taxonomy developed by AIETHICS in their analysis of ethics statements to each of the reports. As shown in Figure 1 , these 12 categories were sufficient to match almost all of the core principles across reports, with two exceptions: several reports advocated for the value of organizational or institutional sustainability, as distinct from the environmental sustainability category; And the EAD referenced a number of principles, such as “competence” and (technical) “dependability” that generally referred to the value of sound engineering.

Relationship among values. * Denotes an extension to the core categorization developed in Jobin et al. ( 2019 ).

The value of transparency acts as a least-common-denominator across reports (as shown in Figure 2 ). However, transparency never appeared alone and was most often included with social equity and solidarity or inclusion. These values are distinct, and some, such as privacy and transparency, are in direct tension.

Common core of values. * Denotes an extension to the core categorization developed in Jobin et al. ( 2019 ).

A dramatic expression of science's dependency on the values embedded in the knowledge ecosystem is the “reproducibility crisis” that has emerged at the interface of science practice and science communication (NASEM-BCBSS, 2019 ). Reproducibility is essentially a function of transparent scientific information management (Freese and King, 2018 ), contributing to meta-science, which furthers the values of equity and inclusion as much as those of interpretability and accountability. Open science enhances scientific reliability and human well-being by increasing access to both the process and the fruits of scientific research.

The values inherent in science practices also include the processes of assigning and rewarding value in research, which are themselves functions of science information management: this is the charge that those developing alternatives to bibliometric indicators should accept. Academic organizations determine the perceived value and impact of scholarly work by allocating attention and resources through promotion and tenure processes, collection decisions, and other recognition systems (Maron et al., 2019 ). As we have learned with economic growth or productivity measures, mechanistic indicators of success do not necessarily align with social and ethical values. Opaque expert and technical systems can undermine public trust unless the values inherent in their design are explicit and communicated clearly (IEEE Global Initiative et al., 2019 ).

When the academy delegates governance of the scholarly knowledge ecosystem to economic markets, scholarly communication tends toward economic concentration driven by the profit motives of monopolistic actors (e.g., large publishers) and centered within the global north (Larivière et al., 2015 ). The result has been an inversion of the potential for equity and democratization afforded by technology, leading instead to a system that is:

“plagued by exclusion; inequity; inefficiency; elitism; increasing costs; lack of interoperability; absence of sustainability and/or durability; promotion of commercial rather than public interests; opacity rather than transparency; hoarding rather than sharing; and myriad barriers at individual and institutional levels to access and participation.” (Altman et al., 2018 , p. 5)

The imperative to bring the system under a different values regime requires an explicit and coordinated effort that is generated and expressed through research. The reports here reflect the increasing recognition that these values must also inform information research.

Despite emerging as a “loose, feel-good concept instead of a rigorous framework” (Mehra and Rioux, 2016 , p. 3), social justice in information science has grown into a core concern in the field. Social justice—“fairness, justness, and equity in behavior and treatment” (Maron et al., 2019 , p. 34)—may be operationalized as an absence of pernicious discrimination or barriers to access and participation, or affirmatively as the extension of agency and opportunity to all groups in society. A dearth of diversity in the knowledge creation process (along the lines of nationality, race, disability, or gender) constrains the positive impact of advances in research and engineering (Lepore et al., 2020 ).

Many vital areas of the scientific evidence base, the legal record, and broader cultural heritage are at substantial risk of disappearing in the foreseeable future. Values of information durability must be incorporated into the design of the technical, economic, and legal systems governing information to avoid catastrophic loss (NDSA, 2020 ). The unequal exposure to the risk of such loss is itself a source of inequity. Durability is also linked to the value of sustainability , applying both to impact the global environment (Jobin et al., 2019 ) and the durability of investments and infrastructure in the system, ensuring continued access and functioning across time and space (Maron et al., 2019 ).

As the information ecosystem expands to include everyone's personal data, the value of data agency has emerged to signify how individuals “ensure their dignity through some form of sovereignty, agency, symmetry, or control regarding their identity and personal data” (IEEE Global Initiative et al., 2019 , p. 23). The scale and pervasiveness of information collection and use raises substantial and urgent theoretical, engineering, and design questions about how people, systems, and environments create, access, use, curate, and sustain information.

These questions further implicate the need for core values to govern information research and use: if individuals are to be more than objects in the system of knowledge communication, their interaction within that system requires not only access to information but also its interpretability beyond closed networks of researchers in narrow disciplines (Altman et al., 2018 ; NDSA, 2020 ). Interpretability of information is a prerequisite for the value of accountability , which is required to assess the impacts and values of scholarship. Accountability also depends on transparency, as the metrics for monitoring the workings of the scholarly knowledge ecosystem cannot perform their accountability functions unless the underlying information is produced and disseminated transparently.

Scholarly Knowledge Ecosystem Research Challenges

Governing large information ecosystems presents a deep and broad set of challenges. Collectively, the reports we review touched on a broad spectrum of research areas—shown in Table 4 . These research areas range from developing broad theories of epistemic justice (Altman et al., 2018 ) to specific questions about the success of university-campus strategies for rights-retention (Maron et al., 2019 ). This section focuses on those research areas representing grand challenges — areas with the potential for broad and lasting impact in the foreseeable future.

Research areas.

Altman et al. ( 2018 ) covered the broadest set of research areas. It identified six challenges for creating a scholarly knowledge ecosystem to globally extend the “true opportunities to discover, access, share, and create scholarly knowledge” in ways that are democratic in their processes—while creating knowledge that is durable as well as trustworthy. These imperatives shape the research problems we face. Such an ecosystem requires expanding participation beyond the global minority that dominates knowledge production and dissemination. It must broaden the forms of knowledge produced and controlled within the ecosystem, including, for example, oral traditions and other ways of knowing. The ecosystem must be built on a foundation of integrity and trust , which allows for the review and dissemination of growing quantities of information in an increasingly politicized climate. With the exponential expansion of scientific knowledge and digital media containing the traces of human life and behavior, problems of the durability of knowledge , and the inequities therein, are of growing importance. Opacity in the generation, interpretation, and use of scientific knowledge and data collection, and the complex algorithms that put them to use, deepens the challenge to maintain individual agency in the ecosystem. Problems of privacy, safety, and control, intersect with diverse norms regarding access and use of information. Finally, innovations and improvements to the ecosystem must incorporate incentives for sustainability so that they do not revert to less equitable or democratic processes. 7

We draw from the frameworks of all the reports to identify several themes for information research. Figure 3 highlights common themes using a term-cloud visualization summarizing research areas and research questions. 8 The figure shows the importance that the documents place on the values discussed above and the importance of governance, technology, policy, norms, incentives, statistical reproducibility, transparency, and misuse.

Research problems.

For illustration, we focus on several exemplar proposals that reflect these themes. IEEE Global Initiative et al. ( 2019 ) asks how “the legal status of complex autonomous and intelligent systems” relates to questions of liability for the harms such systems might cause. This question represents a challenge for law and for ethical AI policy, as Jobin et al. ( 2019 ) outlined. Maron et al. ( 2019 ) raise questions about how cultural heritage communities limiting access to their knowledge while also making it accessible according to community standards poses additional problems for AI-using companies and the laws that might govern them. There is a complex interaction of stakeholders at the intersections of law, ethics, technology, and information science, and a research agenda to address these challenges will require interdisciplinary effort across institutional domains.

Consider the Grand Challenge's call for research into the determinants of engagement and participation in the scholarly knowledge ecosystem. Understanding those drivers requires consideration of a question raised by Maron et al. ( 2019 ) regarding the costs of labor required for open-source infrastructure projects, including the potentially inequitable distribution of unpaid labor in distributed collaborations. Similarly, NASEM–BRDI ( 2018 ) and NDSA ( 2020 ) delineate the basic and applied research necessary to develop both the institutional and technical infrastructure of stewardship, which would enable the goal of long-term durability of open access to knowledge. Finally, NASEM-BCBSS ( 2019 ) and Hardwicke et al. ( 2020 ) together characterize the range of research needed to systematically evaluate and improve the trustworthiness of scholarly and scientific communications.

The reports taken as a collection underscore the importance of these challenges and the potential impact that solving them can have far beyond the academy. For example, the NDSA 2020 report clarifies that resolving questions of predicting the long-term value of information and ensuring its durability and sustainability are critical for the scientific evidence-base and for preserving cultural heritage and maintaining the public record for historical government, and for legal purposes. Further, IEEE Global Initiative et al. ( 2019 ) and Jobin et al. ( 2019 ) demonstrate the ubiquitous need for research into effectively embedding ethical principles into information systems design and practice. Moreover, the IEEE report highlights the need for trustworthy information systems in all sectors of society.

Commonalities Across the Recommended Solution Approaches to Core Scientific Questions

The previous section demonstrates that strengthening scientific knowledge's epistemological reliability and social equity implicates a broad range of research questions. We argue that despite this breadth, three common themes emerge from the solution approaches in these reports: the need for multi-sectoral cooperation and coordination; the need for mixed methods analysis at multiple levels; and the need for interdisciplinary collaboration.

Cooperate Across Sectors to Intervene and Measure at Scale

As these reports reiterate, information increasingly “lives in the cloud.” 9 Almost everyone who creates or uses information, scholars included, relies on information platforms at some point of the information lifecycle (e.g., search, access, publication). Further, researchers and scholars are generally neither the owners of, nor the most influential stakeholder in, the platforms that they use. Even niche platforms, such as online journal discovery systems designed specifically for dedicated scholarly use and used primarily by scholars, are often created and run by for-profit companies and (directly or indirectly) subsidized and constrained by government-sector funders (and non-profit research foundations).

A key implication of this change is that information researchers must develop the capacity to work within or through these platforms to understand information's effective properties, our interactions with these, the behaviors of information systems, and the implications of such properties, interactions, and behaviors for knowledge ecosystems. Moreover, scholars and scientists must be in dialogue with platform stakeholders to develop the basic research needed to embed human values into information platforms, to understand the needs of the practice, and to evaluate both.

Employ a Full Range of Methodologies Capable of Measuring Outcomes at Multiple Levels

Many of the most urgent and essential problems highlighted through this review require solutions at the ecosystem (macro-) level. 10 In other words, effective solutions must be implementable at scale and be self-sustaining once implemented. A key implication is that both alternative metrics and vastly greater access to quantitative data from and about the performance of the scholarly ecosystem are required. 11

Engage Interdisciplinary Teams to Approach Ecosystem-Level Theory and Design Problems

Selecting, adapting, and employing methods capable of reliable ecosystem-level analysis will require drawing on the experience of multiple disciplines. 12 Successful approaches to ecosystem-level problems will, at minimum, require the exchange and translation of methods, tools, and findings between research communities. Moreover, many of the problems outlined above are inherently interdisciplinary and multisectoral—and successful solutions are likely to combine insights from theory, method, and practice from information- and computer- science, social- and behavioral- science, and from law and policy scholarship.

These three implications reflect broad areas of agreement across these reports regarding necessary conditions for approaching the fundamental scientific research questions about the scholarly knowledge ecosystem in general. Of course these three conditions are necessary, but far from sufficient—and only scratch the surface of what will be needed to restructure the ecosystem. Developing a comprehensive proposal for such a restructuring is a much larger project—even if the individual scientific questions we summarize above were to be substantially answered. For details on promising approaches to the individual areas summarized in Table 4 see the respective reports, and especially (Altman et al., 2018 ; Hardwicke et al., 2020 ; NDSA, 2020 ).

Moreover, the development of a blueprint to effectively restructure the scholarly ecosystem will require addressing a range of issues. These include the development of effective science practices; effective advocacy in favor or an improved scholarly ecosystem; the development of model information policies and standards (e.g., with respect to licensing, or formats); the construction and operation of information infrastructure; effective education and training; and processes for allocating research funding in alignment with a better functioning ecosystem. Most of the reports discuss above recognize that these issues are critical to any future successful restructuring, and some—especially (Altman et al., 2018 ; NASEM–BRDI, 2018 ; Maron et al., 2019 ; NASEM-BCBSS, 2019 )—suggest specific paths forward.

Although the function of this review is to characterize the core scientific challenges to understanding the scholarly ecosystem necessary for a restructuring. We note that there is a growing consensus, as reflected by these reports, around a number of operational principles, practices, and infrastructure that many believe necessary for a positive restructuring of the scholarly knowledge ecosystem. The most broadly recognized examples of these include the FAIR principles for scientific data management (Wilkinson et al., 2016 ), the TOP guidelines for journal transparency and openness (Nosek et al., 2015 ), arXiv and the increasingly robust infrastructure for preprints (McKiernan, 2000 ; Fraser et al., 2020 ), and the expansion of the infrastructure for data archiving, citation, and discovery (King, 2011 ; Cousijn et al., 2018 ; NASEM-BCBSS, 2019 ; NDSA, 2020 ) that has been critical to science for over 60 years.

Since its inception, the field of information has been a leader in understanding how information is discovered, produced, and accessed. It is now critical to answer these questions as applied to the conduct of research and scholarship itself.

Over the last three decades, the information ecosystem has changed dramatically. The pace of information collection and dissemination has broadened; the forms of scientific information and systems for managing them have become more complex, and the stakeholders and participants in information production and use have vastly expanded. This expansion and acceleration have placed great stress on the system's reliability and heightened internal and external attention to inequities in participation and impact of scientific research and communication.

More recently, the practices and infrastructure for disseminating and curating scholarly knowledge have also begun to change. For example, infrastructure for sharing communications in progress (see, e.g., in preprints, or through alternative forms of publications) is now common in many fields, as is infrastructure to share data for replication and reuse.

These changes present challenges and opportunities for the field of information. While the field's traditional scope of study has broadened from a focus on individual people, specific technologies, and interactions with specific information objects (Marchionini, 2008 ) to a focus on more general information curation and interaction lifecycles, theories and methods for evaluating and designing information ecologies remain rare (Tang et al., 2021 ). Further, information research has yet to broadly incorporate approaches from other disciplines to conduct large-scale ecological evaluations or systematically engage with stakeholders in other sectors of society to design and implement broadly-used information platforms. Moreover, while there has been increased interest in the LIS field in social justice, the field lacks systematic frameworks for designing and evaluating systems to promote this value (Mehra and Rioux, 2016 ).

For scholarship to be epistemologically reliable, policy-relevant, and socially equitable, the systems for producing, disseminating, and sustaining scientific information must be re-theorized, reevaluated, and redesigned. Because of their broad and diverse disciplinary background, information researchers and schools could have an advantage in convening and catalyzing effective research. The field of information science can make outstanding contributions by thoughtful engagement in multidisciplinary, multisectoral, and multimethod research focused on values-aware approaches to information-ecology scale problems.

Thus reimagined and reengineered through interdisciplinary and multisectoral collaborations, the scientific information ecosystem can support enacting evidence-based change in service of human values. With such efforts, we could ameliorate many of the informational problems that are now pervasive in society: from search engine bias to fake news to improving the conditions of life in the global south.

Author Contributions

The authors describe contributions to the paper using a standard taxonomy (Allen et al., 2014 ). Both authors collaborated in creating the first draft of the manuscript, primarily responsible for redrafting the manuscript in its current form, contributed to review and revision, contributed to the article's conception (including core ideas, analytical framework, and statement of research questions), and contributed to the project administration and to the writing process through direct writing, critical reviewing, and commentary. Both authors take equal responsibility for the article in its current form.

This research was supported by MIT Libraries Open Access Fund.

Conflict of Interest

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.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 Throughout this paper, we follow Altman et al. ( 2018 ) in using the terms “scholarship,” “scholarly record,” “evidence base,” and “scholarly knowledge ecosystem” broadly. These denote (respectively), communities and methods of systematic inquiry aimed at contributing to new generalizable knowledge; all of the informational outputs of that system (including those outputs commonly referred to as “scholarly communications”); the domains of evidence that are used by these communities and methods to support knowledge claims (including quantitative measures, qualitative descriptions, and texts); and the set of stakeholders, laws, policies, economic markets, organizational designs, norms, technical infrastructure, and educational systems that strongly and directly affect the scholarly record and evidence base, and/or are strongly and directly affected by it (which encompasses the system of scholarly communication, and the processes generated by this system).

2 In order to create a review that spanned multiple disciplines while maintaining concision and lasting relevance we deliberately concentrate the focus of the article in three respects: First, we focus on enduring research challenges rather than on shorter-lived research challenges (e.g., with a time horizon of under a decade). Second, we focus on fundamental challenges to scientific understanding (theorizing, inference, and measurement) rather than on cognate challenges to scholarly practice such as the developing of infrastructure, education, standardization of practice, and the mobilization and coordination of efforts within and across specific stakeholders. Third, we limit discussion of solutions to these problems to describing the contours of broadly applicable approaches—rather than recapitulate the plethora of domain and problem-specific approaches covered in the references cited.

3 For prominent examples of nanopublication, overlay journals, preprint servers and massive dynamic community databases see (respectively) (Lintott et al., 2008 ; Groth et al., 2010 ; Bornmann and Leydesdorff, 2013 ; Fraser et al., 2020 ).

4 For replication purposes, this database and the code for all figures and tables, are available through GitHub https://doi.org/10.7910/DVN/DJB8XI and will be archived in dataverse before publication, and this footnote will be updated to include a formal data citation.

5 Almost all of the reports stated these values explicitly and argued for their necessity in the design and practice of science. The one exception is (Hardwicke et al., 2020 )—which references core values and weaves them into the structure of its discussion—but does not argue explicitly for them.

6 This set of ethical values constitute ethical principles for scientific information and its use. This should be distinguished from research programs such as (Fricker, 2007 ; Floridi, 2013 ) who propose ethics of information—rules that are inherently normative to information, e.g., Floridi's principle that “entropy ought to be prevented in the infosphere.”

7 Any enumeration of grand challenge problems inevitably tends to the schematic. This ambitious map of challenges, intended to drive research priorities, has the benefit of reflecting the input of a diverse range of participants. Like the other reports in our review, Altman et al. ( 2018 ) lists many contributors (14) from among even more (37) workshop participants, and followed by a round of public commentary. Such collaboration will also be required to integrate responses, as these challenges intertwine at their boundaries. Thus, successful interventions to change the ecosystem at scale will require working in multiple, overlapping problem areas. Notwithstanding, these problems are capacious enough that any one of them could be studied separately and prioritized differently by different stakeholders.

8 Figure 3 is based on terms generated through skip n-gram analysis and ranked by their importance within each document relative to the entire corpus. Specifically, the figure uses TIF * DF (term frequency by inverse document frequency) to select and scale 2 by 1 skip-n-grams extracted from the entire corpus after minimal stop-word removal. This results in emphasizing pairs of words such as “transparency reproducibility” that do not appear in most documents overall, but appear together frequently within some documents.

9 Specifically, see NASEM-BIRDI (2018, chapters one and two), Lazer et al. ( 2009 ), and NDSA et al. (2020, sections 1.1, 4.1, and 5.2).

10 Ecosystem-level analysis and interventions are an explicit and central theme of Altman et al. ( 2018 ), NASEM–BRDI ( 2018 ), Maron et al. ( 2019 ) and Hardwicke et al. ( 2020 ) refer primarily to ecosystems implicitly in emphasizing throughout on the global impacts of and participation in interconnected networks of scholarship. NDSA ( 2020 ) explicitly addresses ecosystem issues through discussion of shared technical infrastructure and practices (see section 4.1) and implicitly through multi-organizational coordination to steward shared content and promote good practice.

11 Metrics are a running theme of IEEE Global Initiative et al. ( 2019 )—especially the ubiquitous need for open quantitative metrics of system effectiveness and impact, and the need for new (alternative) metrics to capture impacts of engineered systems on human well-being that are currently unmeasured. Altman et al. (2018, see, e.g., section 2) notes the severe limitations of the current evidence base and metrics for evaluating scholarship and the functioning of the scholarly ecosystem. Jobin et al. (2019, p. 389) also note the importance of establishing a public evidence base to evaluate and govern ethical AI use. Similarly, NDSA et al. (2020, section 5.2) emphasize the need to develop a shared evidence base to evaluate the state of information stewardship. Maron et al. ( 2019 ) call for new (alternative) metrics and systems of evaluation for scholarly output and contents as a central concern for the future of scholarship (p. 11–13, 16–20). NASEM–BRDI ( 2018 ), NASEM-BCBSS ( 2019 ), and Hardwicke et al. ( 2020 ) emphasize the urgent need for evidential transparency in order to evaluate individual outputs and systemic progress toward scientific openness and reliability—and emphasize broad sharing of data and software code.

12 IEEE Global Initiative et al. ( 2019 ) emphasized interdisciplinary research and education as one of the three core approaches underpinning ethical engineering research and design (pp. 124–129), and identifying the need for interdisciplinary approaches in specific key areas (particularly engineering and well-being, affective computing, science education, and science policy). Altman et al. ( 2018 ) emphasize the need for interdisciplinarity to address grand challenge problems, arguing that an improved scholarly knowledge ecosystem “will require exploring a set of interrelated anthropological, behavioral, computational, economic, legal, policy, organizational, sociological, and technological areas.” Maron et al. (2019, sec. 1) call out the need for situating research in the practice and the engagement of those in the information professions. NDSA ( 2020 ) argue that solving problems or digital curation and preservation require transdisciplinary (sec. 2.5) approaches and drawing on research from a spectrum of disciplines, including computer science, engineering, and social sciences (sec 5). NASEM-BCBSS ( 2019 ) note that reproducibility in science is a problem that applies to all disciplines. While NASEM–BRDI ( 2018 ) and Hardwicke et al. ( 2020 ) both remark that the body of methods, training, and practices (e.g., meta-science, data science) required for achieving open and reproducible (respectively) science require approaches that are inherently inter-/cross-disciplinary.

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• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

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Metrics details

Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

Peer Review reports

Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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Big Data in Education: Pedagogy and Research pp 3–37 Cite as

Big Data in Academic Research: Challenges, Pitfalls, and Opportunities

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Part of the book series: Policy Implications of Research in Education ((PIRE,volume 13))

Big Data are a product of the computer era, enabling the knowledge economy, in which academic researchers are key players, although researchers have been slow to adopt Big Data as a source for academic enquiry. This may be in part because Big Data are curated by commercial or governmental entities, not by researchers. Big Data present several challenges to researchers, including those associated with the size of the data, the development and growth of data sources, and the temporal changes in large data sets. Further challenges are that Big Data are gathered for purposes other than research, making their fit-for-purpose problematic; that Big Data may easily lead to overfitting and spuriousness; and the biases inherent to Big Data. Linkage of data sets always remains problematic. Big Data results are hard to generalize, and working with Big Data may raise new ethical problems, even while obviating old ethical concerns. Nonetheless, Big Data offer many opportunities, allowing researchers to study previously inaccessible problems, with previously inconceivable sources of data. Although Big Data overcome some of the challenges of small data studies, Big Data studies will not supplant small data studies—these should work in concert, leading to real-world translation that can have a lasting impact.

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This project is partially funded by the National Health and Medical Research Council (NHMRC) through the Translational Australian Clinical Toxicology Program (TACT) (grant ID1055176).

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Raubenheimer, J. (2021). Big Data in Academic Research: Challenges, Pitfalls, and Opportunities. In: Prodromou, T. (eds) Big Data in Education: Pedagogy and Research . Policy Implications of Research in Education, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-76841-6_1

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New Challenges in the Research of Academic Achievement: Measures, Methods, and Results

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The New Challenges Facing Academic Researchers

Researchers in higher education are under constant pressure. They face complex funding challenges, increasingly robust compliance rules, new institutional demands, and technological advances. They must adapt to all these developments, make their work more accessible to colleagues, and demonstrate its reach to institutional decision-makers – while not short-changing the actual research and teaching they are doing.

Alterline , an independent research agency, was commissioned by Ex Libris to assess the experience of researchers and senior members of university research offices in conducting and supporting the production of research at institutions of higher education. The survey findings indicate several challenges faced by researchers in a landscape undergoing serious changes.

Too Much Stress, Too Little Time

“I am a little burned out and do not see the benefit of continuous research, just for the sake of surviving in academia.” — Faculty staff, Natural Sciences, United States

According to the feedback received in the survey, academic researchers face a significant challenge in finding enough time for all the activities necessary to both conduct and manage their research. Researchers describe feeling tension not only professionally – between research, teaching, and administrative responsibilities – but even in terms of an inability to maintain a work-life balance. Supporting this claim, members of the  Ex Libris Research Advisory Council report that researchers at their institutions are investing around 45% of their time just in administrative tasks.

The research office and the library play key roles in a range of researchers’ tasks. Researchers report feeling supported by both the research office (81%) and the library (80%) at their institution.

So, how can we explain this apparent discrepancy between clear satisfaction with institutional support and reported feelings of too much stress and too little time?

Perhaps this discrepancy is because researchers undertake the majority of the tasks on their own, even in areas where the research office and library can clearly provide valuable expertise. Examples include the management of article processing charges (APC), which 47% of researchers handle on their own, depositing to institutional repositories (45%), finding funding opportunities (52%), preparing data management plans (54%), ensuring open-access compliance (55%), and monitoring research impact (61%).

Finding Research Grants & Funding

There is “constant pressure for funding money.” — Post-doctoral staff, Natural Sciences, Australia

Not only do over half of the researchers polled find funding opportunities without assistance from their research office, just over half of them also go through the application process for grants independently. According to the study, only 35% of researchers thought it was easy to find relevant funding opportunities and only 32% said it was easy to apply for them.

The study found that university research offices tend to prioritize support for larger grants or for academic “rock stars” who pulled in more funding in the past. Is it any wonder over four in ten academics reported difficulties in finding funding opportunities?

Demonstrating Impact

“When my research actually makes a difference, it is very satisfying.” — Post-doctoral fellow, Social Sciences, United Kingdom

According to the Alterline study, 86% of researchers said they are always or sometimes required to demonstrate the impact of their work.

Demonstrating the value of research projects is increasingly important for the reputation of the institution and the researcher, as well as for justifying future external and internal resource allocations. Yet, the best methods of meaningfully measuring it are still unclear. This is because the true impact is sometimes only evident over time and because isolating its effects can be a serious challenge.

Nonetheless, knowing how they have made a difference is what drives many researchers.

When asked what they enjoyed most about their role, researchers highlighted the application of their work in the real world. An analysis of the study suggests that providing information on the impact of research outside academia may have a positive effect on the retention of researchers by promoting feelings of greater engagement and fulfillment.

Showing Your Work: The Data

“There’s no point in doing something if you’re not excellent.” — Vice-Provost, United States

The quality of the research underway at universities is dependent on the resources available, the processes in place, and the scholars involved. However, gaining recognition of that quality takes a bit more effort.

Advances in technology have changed the demands for transparency in sharing research. The report revealed that most scholars (almost 60%) are now obligated to make their raw research datasets openly available with their published work. However, over a quarter of them (26%) find it difficult to do so in the context of current research data management solutions.

Showing Your Work: Researcher Profiles

A key part of showcasing research is keeping researcher profiles current, complete, and accessible. But there are significant challenges in doing so. Not least of these is that researcher profiles are scattered across many channels, led by LinkedIn (65%), the researcher’s university page (54%), and Google Scholar (42%). One in five researchers report using five or more profiles at the same time across a range of platforms.

Of the researchers surveyed, while 87% were aware of their institution’s researcher profile system, only 55% said they used it – a gap of a third who are not using such institutional systems. (It should be noted that the survey did not ask whether those who do use the institutional system actually keep their institutional profiles up to date.) The lack of use of institutional profile systems may be due to the various third-party platforms researchers use to publicize on their work, as well as their reported lack of time for what they see as an essentially administrative task.

Promoting a ‘Culture of Research’

In a report reviewing five years of progress in its strategic development, the University of Cape Town in South Africa stated that “support from the research office helped promote a culture of research at the university….”

That “culture of research” is another factor that influences the motivation and availability of resources for scholars who seek to invest their time and effort in research. In the coming blog entries, we will be delving deeper into the role of the research office and the library in meeting the challenges faced by researchers in the changing academic environment.

For more details, you can read the full Ex Libris study . Learn about the integrated library system here.

Update, November 2021: 

The Ex Libris 2021 research survey of 300+ researchers and 100+ research office leaders highlights their challenges during COVID-19. The report is freely accessible here.

Academic Problems and Skills

Learning Problem, Learning Skill

Reviewed by Psychology Today Staff

Every school wants every child under its charge to receive the same educational opportunities. However, some students develop academic problems that may cause them to underachieve and, in extreme cases, drop out of school entirely. These problems include confusion about or disinterest in a subject, time management (including procrastination ), lack of attention from teachers, bullying , and inappropriate or violent behavior toward others. While many academic problems can be resolved if caught early and tackled with the help of professionals, some difficulties can persist for years, wreaking havoc on the student’s self-esteem and social relationships.

Conversely, academic skills can be protective influences for students, driving them to achieve their goals . Examples include staying organized, using time wisely, prioritizing effectively, concentrating on tasks, and keeping motivated. With all of the responsibilities that students need to manage at school and at home, these abilities are essential to their success.

• Signs and Causes of Trouble
• School and Mental Health
• Academic Strengths

Teachers and parents are often the first line of defense against academic problems. They should be attuned to any changes in the child’s behavior—such as a drop in grades, a sudden lack of interest in the classroom or at home, skipping classes, or a tendency to act out with teachers or peers or family members. These behaviors may signify an underlying cause that needs to be addressed. It’s important for adults to identify any contributing factors of trouble. A person’s physical health can have a powerful effect on academic performance, and vice versa. If adults see concerning behavior, they should find out if the student is experiencing a lack of sleep, poor nutrition , chronic illness , or a newly developed loss of vision or hearing.

Academic problems can also indicate a possible learning disability, such as  dyslexia  or  ADHD . In such cases, student performance may benefit from school accommodations, such as extra time on tests or additional visual or auditory learning aids for lessons. Parents may also want to consider whether there is a better fit for their child, including  placement in a special education classroom  with fewer students and specially trained staff.

Children and teens are more anxious than ever before. Risk factors like poor sleep and exposure to violence are on the rise. Parents and school personnel  need to know  how to identify the warning signs, deploying intervention immediately to prevent at-risk students from harming themselves and others.

Trouble at school can be both a symptom and a risk factor of mental health conditions. Students today are under extraordinary pressure to excel at academics and extracurriculars, leading to massive stress and worry. When students feel like they cannot measure up to these high and unfair standards, they may develop problems ranging from general anxiety to depression to thoughts of suicide .

Students also have to cope with greater insecurity and a feeling that the world is less safe, given the rise of political instability and violence at schools, particularly mass shootings . This can cause great emotional upset and make it difficult to perform at peak levels.

Ordinary disciplinary measures increase aggression in students. And in turn, this increases everyday violence. Students and teachers are on edge. Plus, anxiety about the possibility of a school shooting is common among students, parents, and school staff, despite the additional safety precautions that many schools are taking.

Students can build up resilience to the stress and anxiety they feel daily in several ways. They can cultivate strong relationships with their peers and teachers and look for opportunities to maximize their strengths as well as engage with the school community. They should also pay attention to their bodies and understand the importance of self-care—getting enough rest, hydrating, eating healthy foods, and exercising. 

Healthy ways to address these fears include making sure students have accurate information, keeping an open line of communication, asking questions rather than making statements, discussing emotions in an age-appropriate manner, and having students describe what would make them feel safer. 

Students who are confident and successful in school tend to share particular academic strengths. While some of these skills may come more easily than others, with time and attention, students can build up their proficiency in most of these areas. Not only do colleges and universities look for these qualities when considering applicants, but they also will serve people well as they transition into the workforce and adult life. Important academic strengths include:

Attention to Detail: Someone with this strength is able to follow instructions while making fewer mistakes. They find it easier to focus on the task at hand and complete an assignment.

Cognitive Flexibility: The most successful students are cognitively flexible and able to adapt to new challenges and changes. A flexible mind moves from task to task, applying what was previously learned to new contexts and problems.

Communication: Humans are social by nature and communicating well, both orally and in writing, is crucial. The ability to clarify goals and expectations furthers cooperation .

Creativity : Innovation and the ability to think more abstractly are in high demand both academically and professionally. Creative thinkers generate great works of art as well as smart solutions to modern problems. Technology evolves daily and creativity is a valuable strength.

Critical Thinking and Problem Solving: Over time, students may forget the details of what they formally studied. However, the ability to think critically allows one to learn efficiently, identify problems, find solutions, evaluate progress, and make plans for the future.

Organization: Students must juggle competing demands, including school assignments, exams, extracurricular activities, family obligations, relationships, and more. Planning ahead, stating clear goals, prioritizing tasks, and managing time can help organizational skills.

Passion: Being thoroughly engaged in a subject can be a powerful motivation for students. A person who is curious and enthusiastic shows a commitment to learning, a positive quality that stands out.

Resilience : Even the best students face academic challenges. After a failure, the resilient learn from their failures and bounce back. They persevere and overcome new obstacles readily.

Unlock your career confidence with these five tips for sustainable career success.

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As handwriting skill wanes states push back and readers respond.

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Are video games always a waste of time? Can children really benefit from staring at a screen?

New research across 124 physics classrooms finds that a brief belongingness intervention can have remarkable outcomes, but only if classrooms are gender diverse.

Can AI look beyond human capabilities to reveal a superreality?

The next steps in gaining the support your child with dyslexia needs at school.

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Meet student researchers solving real-world challenges

Asu engineering researchers present their findings at the fulton forge student research expo.

Henry Nakaana, a civil engineering major in the Ira A. Fulton Schools of Engineering at Arizona State University, works with samples of soil and fungus to create a solution for soils affected by wildfire as a participant in the Fulton Undergraduate Research Initiative program. Nakaana is one of many student researchers helping to solve real-world problems with hands-on research. Photographer: Erika Gronek/ASU

Developing sustainable solar energy solutions, deploying fungi to support soils affected by wildfire, making space education more accessible and using machine learning for semiconductor material discovery — these are just some of the ways students at Arizona State University are addressing real-world challenges through hands-on research.

At the Ira A. Fulton Schools of Engineering at ASU, undergraduate and graduate students have several opportunities to conduct use-inspired research in which they can apply their classroom knowledge and build new skills. Through individual projects mentored by Fulton Schools faculty members, students deliver innovation that matters in the research themes of data science, education, energy, health, security, semiconductor manufacturing and sustainability.

The Fulton Undergraduate Research Initiative, or FURI , and the Master’s Opportunity for Research in Engineering, or MORE , programs give participants valuable experiences in which they conceptualize ideas, develop plans and investigate their research questions over a semester.

Students participating in the Grand Challenges Scholars Program, or GCSP , can apply for additional funding to conduct research through the GCSP research stipend program . Conducting research is part of a GCSP student’s rigorous competency requirements designed to prepare them to solve complex global societal challenges.

These three programs enhance students’ ability to innovate, think independently and solve problems in their communities. They also benefit from the technical and soft skills they gain, which prepare them for their careers and the pursuit of advanced degrees.

Each semester, students who participate in FURI, MORE and the GCSP research stipend program are invited to present their findings at a poster session. The Fulton Forge Student Research Expo is the culmination of the students’ hard work to forge meaningful research paths and connections to make an impact.

Learn about four students who are participating in the spring 2024 Fulton Forge Student Research Expo. Meet them and more than 100 other student investigators at the event, which is open to the public, on Friday, April 19, from 1 to 3 p.m. at the Student Pavilion on the ASU Tempe campus.

Clara Chaves Azevedo

Civil engineering major Clara Chaves Azevedo is conducting research to reduce the toxicity of promising perovskite materials to implement solar energy generation on windows with faculty mentor Nick Rolston , an assistant professor of electrical engineering. Chaves Azevedo is a second-semester first-year student — the earliest a Fulton Schools student can participate in FURI.

Question: How will your engineering research project impact the world?

Chaves Azevedo: The project as a whole is a new opportunity to collect more solar energy and make it more accessible. Solar energy is clean and cheap; therefore, it will positively impact how society gathers energy and reduces the use of sources that harm the environment. My specific research is to find an even more sustainable solution. Instead of using lead to produce the panels, I propose using tin, making it a less toxic option.

Q: What has been your most memorable experience as a student researcher in this program? Did you have a particular “aha!” moment during your project?

Chaves Azevedo: My most memorable experience was being able to see what my samples actually looked like under a microscope. It was fascinating to see how something that looks so simple to the naked eye can appear so complex and beautiful under a microscope.

Q: How do you see this experience helping with your career or advanced degree goals?

Chaves Azevedo: As a civil engineer with a focus on sustainability, my ambition is to harmoniously integrate the principles of environmental consciousness and engineering expertise to construct homes that minimize adverse impacts on our planet.

In addition to the intrinsic value of engaging in research, I find that the experience enriches my skill set by fostering creativity, honing research capabilities and refining my communication aptitude, among other things.

Furthermore, I am particularly enthralled by the prospect of incorporating solar panel windows into residential construction, as it represents a tangible manifestation of my commitment to sustainable innovation and architectural excellence.

Henry Nakaana

Civil engineering first-year student Henry Nakaana’s FURI project with Emmanuel Salifu , an assistant professor of civil and environmental engineering, involves engineering fungi that could help support areas affected by increasingly frequent wildfires and heavy rains that make soil vulnerable to erosion and landslides. 

Q: What made you want to get involved in FURI and the project you’re working on?

Nakaana: I joined FURI to gain experience in research, obtain knowledge that can help me improve my career and technical engineering skills, and also contribute valuable insights to the scientific community. My project explores the engineered growth of fungi as a novel method to improve stability and erosion resistance on post-wildfire soils. I chose this project because it aligns with my passion for using nature-based solutions to solve engineering problems.

Q: How will your engineering research project impact the world?

Nakaana: Today the occurrence of wildfires is expected to increase greatly in the northern parts of the U.S. and Canada, which will leave soil exposed to erosion and landslides. My research project offers an effective, budget-friendly and fast way to protect post-wildfire soils compared to other available solutions. My project also shows the additional advantages of using fungi on soil such as carbon sequestration, which helps reduce global climate change.

Q: What has been your most memorable experience as a student researcher?

Nakaana: One of my most memorable moments was when I realized the initial fungi growth on the soil. It was so amazing how fungi can grow in as little as two days to cover the whole soil surface. It made me feel more like an explorer uncovering secrets in a scientific space whose potential has not been discovered.

Nakaana: The unique combination of engineering and ecological considerations in this project aligns perfectly with my career aspirations to use sustainable technology. Furthermore, this experience not only adds depth to my academic qualifications but also establishes a valuable network within the scientific community on a research topic that is just growing globally.

Collaborating with experts in the field through my mentor has broadened my perspective and allowed me to contribute to cutting-edge research. These aspects, combined with the tangible outcomes of the project, will increase my credibility and open doors to opportunities for further education and advancement in my engineering career.

Ritwik Sharma

Aerospace engineering sophomore Ritwik Sharma is working on a cross-disciplinary project with Daniel C. Jacobs , an assistant professor in the ASU School of Earth and Space Exploration , to share his passion for space. His work, funded by the GCSP research stipend opportunity, involves developing a wayfinding method for the Completely Hackable Amateur Radio Telescope , known as CHART, an inexpensive and simple way to use radio waves to see features in outer space not visible to the human eye. He aims to make the system more accessible for individuals and K–12 classroom instruction.

Q: How will your research project impact the world?

Sharma: The guidance system I am designing for CHART will give K–12 students a way to understand how astronomers observe different parts of the universe with radio telescopes. This will improve their understanding of astronomy as a field by teaching them how astronomers locate objects in the sky, and why they have to rely on coordinates as opposed to general directions.

Since the Earth is constantly revolving around the sun, our frame of reference is constantly changing, which makes coordinate systems like right ascension and declination and galactic an important part of the subject because they make tracking objects like galaxies and nebulae easier.

Sharma: I plan to pursue a doctorate after I graduate from ASU, and I see this experience helping me by showing my future supervisors that I am capable of pursuing a project on my own, under the instruction of a mentor. This arrangement is common in graduate school where students work under the direction of their advisor on projects like their theses, dissertations or other items necessary to complete their doctoral or master’s degree. By showing my ability to work efficiently on a project like this, I can also show my future thesis advisor that I will be perfectly capable of completing something like a doctoral thesis, which will give me an advantage over other applicants.

Aishwarya Katkar

Mechanical engineering graduate student Aishwarya Katkar says her degree program provides a versatile foundation that can make meaningful impacts in many industries, including semiconductor manufacturing. In her research with the MORE program under the guidance of Masoud Yekani Fard , an assistant teaching professor specializing in mechanical and aerospace engineering, Katkar is using machine learning to help expedite material analysis to develop semiconductor device components.

Katkar: One of the primary challenges in materials testing and analysis is the significant time investment required. We aim to implement machine learning techniques to expedite the process. Our goal is to automate the identification of novel materials with specific properties by analyzing nanoparticles and the spaces between them. This approach will enable us to understand various material characteristics, such as electrical conductivity, more efficiently.

By streamlining the material analysis process, we can enhance the quality of materials used in semiconductor manufacturing. This innovative method will save time by eliminating the need for manual analysis, resulting in a smoother and more efficient process overall.

Q: How do you see this experience helping with your career/advanced degree goals?

Katkar: This experience holds immense significance for my career trajectory, particularly as a mechanical engineer with a background primarily rooted in design software. While my expertise in design software is undoubtedly valuable, this research opportunity has helped me broaden my horizons, particularly in the realm of material science. Integrating machine learning into material analysis has not only deepened my understanding of this critical aspect of engineering but has also expanded my skill set in the emerging field of machine learning.

This experience has made me confident in my ability to learn independently and has better equipped me to tackle multifaceted engineering challenges and make meaningful contributions to innovative projects in the field. Furthermore, it enhances my qualifications for a master's degree program and diverse career opportunities, bridging the gap between my existing skills and the broader knowledge essential for success in mechanical engineering.

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3 Ways to Engage with Trending Academic Programs

• April 22, 2024
• Topic: Academic Program Development , Enrollment Management , Higher Ed Trends , Higher Education
• Resource type: Insights Blog

Learn how trending academic programs play an important role in addressing today’s student enrollment challenges.

Higher education leaders are all too familiar with prospective students’ growing doubts about the return on investment of a college education. Concerns about tuition affordability, student debt, and future employment challenges are chief reasons why many potential students are opting out of a traditional four-year degree. For institutions facing tough enrollment challenges, administrative leaders must turn to more nimble and creative solutions to uncover top academic program opportunities. Beyond marketing messages or branding campaigns, one of the best ways to attract new students is to ensure your academic programs meet both student demand and future employer needs. Here are three research-driven ways to ensure your degree and certificate offerings will bring more students to your door.

Embrace Flexible Learning Options and Alternative Credentials

Flexible learning options such as online, hybrid, part-time, and career-focused offerings such as microcredentials remain a trending topic in higher education and a proven way to accommodate more non-traditional students such as adults over 24, caregiving students, and students with full-time employment needs. Colleges and universities that embrace trending academic program models and modalities are better positioned to attract and retain students looking for a strong return on their educational investment.

Some institutions may experience pushback from faculty or staff about offering non-degree programs or more flexible modalities. However, most non-traditional offerings cater to different populations than traditional degree programs. Furthermore, overlooking their potential may mean overlooking powerful opportunities to meet student demand. Having robust and varied academic programming means that your institution can appeal not only to traditional degree-seeking students but also to people in need of career upskilling who would otherwise not ever consider attending.

Consider the following takeaways when aiming to diversify the credential offerings and course formats at your institution:

• Form partnerships with local employers to learn more about how to meet emerging industry needs and/or spread awareness about your current offerings.
• Survey prospective students to understand their needs and interests around academic programs, learning modalities, and potential barriers to enrollment.
• Incorporate research into your academic programming strategy to continually investigate degree trends, labor statistics, and job outlook data to identify any programmatic gaps that could be filled with non-traditional learning options or credentials.

Get the latest expert opinions on the state of higher education with our prerecorded webinar, What’s Ahead in 2024: Trends We See in Higher Education.

Track student demand to pinpoint target markets for trending academic programs.

Prospective students are motivated to look for degree and certificate programs that will put them on a sustainable career path that matches their interests and leads to employment. It’s key, therefore, for institutions to highlight career connections and speak to tangible job skills both through new and existing academic programs. Yet having enticing or innovative programs alone is not enough.

Institutions must also take steps to help students discover their top academic programs and match with their best-fit programs. In a sea of educational options, this can be a tall order. Institutions can collect, analyze, and use data to their advantage to identify likely prospective student populations by programmatic areas and focus recruitment efforts on geographies and programs with the most promise. Follow these data-driven best practices to sharpen your institution’s program-specific recruitment efforts:

• Get real-time data on programmatic demand by tracking academic program search engine trends nationally and in your key recruitment regions.
• Explore key demographic and socioeconomic trends and enrollment patterns at your institution by program areas (including historical enrollment data, conversion rates, and completion data) to develop targeted student profiles.
• Identify geographic target markets with favorable recruitment conditions and create a list of promising markets for further investigation.

Learn how Generation Z’s perspectives influence their higher education needs and expectations with our infographic,  9 Tips to Attract Gen Z Students.  

Leverage a competitive advantage with optimized academic programs.

Conducting a comprehensive academic portfolio review  can help you understand how well your degrees and certificates promote institutional goals and meet both student and employer demand. A portfolio review is the process of analyzing the full range of program options to gather insights needed to adjust course in a competitive landscape.

These insights make it easier to establish a more well-rounded portfolio by assessing the performance of current offerings and highlighting opportunities to develop new programs in high-growth, high-demand areas. An academic portfolio review may also uncover trending academic programs and innovative ways to increase non-traditional credentials or learning modalities that target untapped student markets. Leverage the following tips when assessing the efficacy of your institution’s portfolio:

• Define goals for each existing program and create specific metrics to monitor how well programs are achieving these goals.
• Conduct peer benchmarking to understand market saturation and learn how other colleges and universities position similar programs.
• Choose metrics that focus on evolving labor market demands, student demand, and mission fit, to identify what types of new programs have the most potential to perform successfully and differentiate institutional identity.

Gain more insights about measuring the health of your academic offerings with our Step-by-Step Guide to a Comprehensive Academic Portfolio Review .

As enrollment patterns and student needs continue to shift, institutions must use data to make academic program decisions that capture more student interest. Leaders should invest resources in practices that help their institution create a more distinct value proposition to help stand out among peer organizations. The colleges and universities that leverage flexible learning options and alternative credentials, track real-time student demand, and step back to measure their entire portfolio’s performance are the ones that will succeed in a fluctuating higher education landscape.

Top 10 Degrees on the Rise in 2024

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Working moms in the U.S. have faced challenges on multiple fronts during the pandemic

The coronavirus pandemic has created new challenges and reinforced existing ones for many working mothers in the United States. For Mother’s Day, here is a look at American moms’ experiences juggling work and parenting responsibilities during the COVID-19 outbreak , based on data from Pew Research Center surveys.

The data for this analysis was drawn from multiple Pew Research Center surveys, with links included in the text of the post. Most findings are from a Center survey of 10,332 U.S. adults conducted from Oct. 13-19, 2020.

Everyone who took part in the surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This allows for nearly all U.S. adults to have a chance of selection. The surveys are weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

In the early months of the pandemic, there was an increase in the share of mothers who said they preferred not to work for pay at all. In an October 2020 survey , about a quarter (27%) of mothers with children younger than 18 at home said that at that point in their life, the best work arrangement for them personally would be not working for pay at all, up from 19% who said so in a  summer 2019 survey .

The share of mothers who said working full time would be best for them dropped from 51% to 44% during that span, while around three-in-ten in both surveys said they would prefer to work part-time.

Employed moms were more likely than working dads to report experiencing professional hurdles during the pandemic, according to the same October 2020 survey . Among working parents with children under age 18 at home, mothers were generally more likely than fathers to say that, since the beginning of the coronavirus outbreak, they faced a variety of professional challenges.

For example, 54% of working moms said they felt like they could not “give 100%” at work because they were balancing work and parenting responsibilities, compared with 43% of working dads who said this. Working moms were also more likely than dads to say they needed to reduce their work hours because of parenting responsibilities (34% vs. 26%) and to report being treated as if they weren’t committed to their work because they have children (19% vs. 11%).

These patterns mirrored those found before the coronavirus outbreak in the  summer of 2019  when working parents were asked if these things had ever happened to them.

In general, mothers view themselves as shouldering more child care duties than their spouses or partners do, while dads are more likely to say these responsibilities are evenly shared, according to the October 2020 survey . About three-quarters of moms in opposite-sex relationships (74%) said they did more to manage their children’s schedules and activities than their spouse or partner; only 3% said their spouse or partner took on more of these responsibilities. Roughly half of mothers (54%) said they did more than their spouse or partner to be an involved parent, while just 3% said their spouse or partner did more.

Most fathers in opposite-sex relationships (63%) said being an involved parent was equally shared between them and their spouse or partner; a smaller share of mothers (43%) said the same. Similarly, fathers were more likely than mothers to say that they and their partner or spouse shared the management of their kids’ schedules and activities (36% vs. 23%). These findings also largely reflect patterns from before the pandemic.

Earlier this year, about half of working parents said the coronavirus outbreak had made it difficult to handle child care responsibilities, and moms were especially likely to report this problem. Around six-in-ten moms (58%) said this had been at least somewhat difficult in recent weeks, compared with 43% of working dads, according to a February 2022 survey of working parents with children younger than 12 at home. These figures were similar to those reported by working moms and dads in October 2020, when many schools and child care centers were  not operating in person .

More than a third of moms who teleworked during the early months of the pandemic said they had a lot of child care responsibilities while working from home, according to the October 2020 survey . Among employed parents who were working remotely all or most of the time and had children younger than 18 at home, 36% of moms said they had a lot of child care duties during this time – roughly double the share of dads who said the same (16%). Moms and dads were about equally likely to say they had at least some child care responsibilities while working from home (66% vs. 65%).

Many moms and dads who worked from home early on in the coronavirus pandemic reported difficulties getting their work done without interruptions, the October 2020 survey found . About half of mothers (52%) and fathers (48%) with children under 18 at home who worked remotely all or most of the time said that since the beginning of the coronavirus outbreak, it had been very or somewhat difficult for them to get their work done without interruptions. A much smaller share of teleworkers without minor children at home (20%) reported the same.

Around six-in-ten moms felt that they spent about the right amount of time with their children in 2020. In an October 2020 survey , 58% of mothers with children under 18 at home said they spent the right amount of time with their children, compared with 28% who said they spent too little time with their kids and 13% who said they spent too much time with them.

Nearly half of fathers (46%) said they spent the right amount of time with their children. A similar share (48%) said they spent too little time with their kids, and only 5% of dads said that they spent too much time with their children.

• Coronavirus (COVID-19)
• Economics, Work & Gender
• Gender & Work
• Gender Roles
• Motherhood & Fatherhood

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, mental health and the pandemic: what u.s. surveys have found, most popular.

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