An official website of the United States government
Abstract This study indicates the most effective combinations of scaffolding features within computer science and technology education settings. It addresses the research question, “What combinations of scaffolding characteristics, contexts of use, and assessment levels lead to medium and large effect sizes among college‐ and graduate‐level engineering and technology learners?” To do so, studies in which scaffolding led to a medium or large effect size within the context of technology and engineering education were identified within a scaffolding meta‐analysis data set. Next, two‐step cluster analysis in SPSS 24 was used to identify distinct groups of scaffolding attributes tailored to learning computer science at the undergraduate and graduate levels. Input variables included different scaffolding characteristics, the context of use, education level, and effect size. There was an eight‐cluster solution: five clusters were associated with large effect size, two with medium effect size, and one with both medium and large effect size. The three most important predictors were the context in which scaffolding was used, if and how scaffolding is customized over time and the decision rules that govern scaffolding change. Notably, highly effective scaffolding clusters are associated with most levels of each predictor.
more »
« less
This content will become publicly available on January 22, 2026
The Landscape of Research on Contextualized Science Learning: A Bibliometric Network Review
The vast and rapidly growing amount of science education research makes it challenging for researchers to navigate and synthesize developments across the field, particularly concerning broad concepts evolving along divergent paths. To address this issue, a novel review methodology employing bibliometrics and network analysis was tested to identify and characterize clusters of research focused on the relationship between school‐based science learning and contexts where that science is applied, experienced, observable, or otherwise relevant (e.g., socio‐scientific inquiry, place‐based learning, culturally‐responsive pedagogy). Using a sample of 935 academic papers, the bibliometric network analysis revealed the landscape of contextualized science learning research, identifying 13 distinct clusters of scholarship. Bibliometric and qualitative data were used to describe the research trends within clusters and confirm they were conceptually meaningful and distinct. This methodology facilitated greater understanding of how research can become clustered into “invisible colleges” over time, offering a synthesis approach to grasp interrelated lines of research within an evolving landscape. The methodology has potential to identify other schools of thought or overarching themes in science education, enhancing researchers’ ability to perceive the field as a coherent landscape of interconnected ideas or to identify specific research trajectories within a broad concept.
more »
« less
- Award ID(s):
- 1937772
- PAR ID:
- 10567871
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Science Education
- ISSN:
- 0036-8326
- Subject(s) / Keyword(s):
- Bibliometrics Science Learning contextualization landscape study
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This Work-in-Progress paper in the Research Category explores the unique challenges and opportunities of interdisciplinary education in computational modeling for life sciences student researchers at emerging research institutions (ERIs), specifically in predominantly undergraduate institutions (PUIs), and minority serving institutions (MSIs). Engineering approaches such as computational modeling have underappreciated potential for capacity building for the biomedical research enterprises of ERIs. We perform a bibliometric analysis to assess the prevailing use of computational modeling in life sciences research at MSIs, and PUIs. Additionally, we apply Social and Cognitive Theory to identify unique attitudinal, social and structural barriers for student researchers in learning and using computational modeling approaches at each of these types of institutions. Specifically, we use quantitative retrospective pre- and post-survey data and qualitative interviews of students who have attended a short-format computational modeling training course. We supplement these data with qualitative interviews of the students' faculty sponsors. Upon completion, this study will provide deeper understanding of issues related to computer science and engineering education at non-Research I institutions.more » « less
-
CONTEXT Engineering education is an interdisciplinary research field where scholars are commonly embedded within the context they study. Engineering Education Scholars (EES), individuals who define themselves by having expertise associated with both engineering education research and practice, inhabit an array of academic positions, depending on their priorities, interests, and desired impact. These positions include, but are not limited to, traditional tenure-track faculty positions, professional teaching or research positions, and positions within teaching and learning centers or other centers. EES also work in diverse institutional contexts, including engineering disciplinary departments, first-year programs, and engineering education departments, which further vary their roles. PURPOSE OR GOAL The purpose of this preliminary research study is to better understand the roles and responsibilities of early-career EES. This knowledge will enable PhD programs to better prepare engineering education graduates to more intentionally seek positions, which is especially important given the growing number of engineering education PhD programs. We address our purpose by exploring the following research question: How can we describe the diversity of academic or faculty roles early-career EES undertake? APPROACH OR METHODOLOGY/METHODS We implemented an explanatory sequential mixed-methods study starting with a survey (n=59) to better understand the strategic actions of United States-based early-career EES. We used a clustering technique to identify clusters of participants based on these actions (e.g., teaching focused priorities, research goals). We subsequently recruited 14 survey participants, representing each of the main clusters, to participate in semi-structured interviews. Through the interviews, we sought to gain a more nuanced understanding of each participant’s actions in the contexts of their roles and responsibilities. We analyzed each interview transcript to develop memos providing an overview of each early-career EES role description and then used a cross case analysis where the unit of analysis was a cluster. ACTUAL OUTCOMES Five main clusters were identified through our analysis, with three representing primarily research-focused day-to-day responsibilities and two representing primarily teaching-focused day-to-day responsibilities. The difference between the clusters was influenced by the institutional context and the areas in which EES selected to focus their roles and responsibilities. These results add to our understanding of how early-career EES enact their roles within different institutional contexts and positions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY This work can be used by graduate programs around the world to better prepare their engineering education graduates for obtaining positions that align with their goals and interests. Further, we expect this work to provide insight to institutions so that they can provide the support and resources to enable EES to reach their desired impact within their positions.more » « less
-
CONTEXT Engineering education is an interdisciplinary research field where scholars are commonly embedded within the context they study. Engineering Education Scholars (EES), individuals who define themselves by having expertise associated with both engineering education research and practice, inhabit an array of academic positions, depending on their priorities, interests, and desired impact. These positions include, but are not limited to, traditional tenure-track faculty positions, professional teaching or research positions, and positions within teaching and learning centers or other centers. EES also work in diverse institutional contexts, including engineering disciplinary departments, first-year programs, and engineering education departments, which further vary their roles. PURPOSE OR GOAL The purpose of this preliminary research study is to better understand the roles and responsibilities of early-career EES. This knowledge will enable PhD programs to better prepare engineering education graduates to more intentionally seek positions, which is especially important given the growing number of engineering education PhD programs. We address our purpose by exploring the following research question: How can we describe the diversity of academic or faculty roles early-career EES undertake? APPROACH OR METHODOLOGY/METHODS We implemented an explanatory sequential mixed-methods study starting with a survey (n=59) to better understand the strategic actions of United States-based early-career EES. We used a clustering technique to identify clusters of participants based on these actions (e.g., teaching focused priorities, research goals). We subsequently recruited 14 survey participants, representing each of the main clusters, to participate in semi-structured interviews. Through the interviews, we sought to gain a more nuanced understanding of each participant’s actions in the contexts of their roles and responsibilities. We analyzed each interview transcript to develop memos providing an overview of each early-career EES role description and then used a cross case analysis where the unit of analysis was a cluster. ACTUAL OUTCOMES Five main clusters were identified through our analysis, with three representing primarily research-focused day-to-day responsibilities and two representing primarily teaching-focused day-to-day responsibilities. The difference between the clusters was influenced by the institutional context and the areas in which EES selected to focus their roles and responsibilities. These results add to our understanding of how early-career EES enact their roles within different institutional contexts and positions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY This work can be used by graduate programs around the world to better prepare their engineering education graduates for obtaining positions that align with their goals and interests. Further, we expect this work to provide insight to institutions so that they can provide the support and resources to enable EES to reach their desired impact within their positions.more » « less
-
null (Ed.)Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still a far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor-descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.more » « less
