Dataset Information

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.

ABSTRACT: Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self-efficacy, and sense of social belonging in a large (more than 250 students) introductory STEM course. A transition to active learning closed the gap in learning gains between non-URM and URM students and led to an increase in science self-efficacy for all students. Sense of social belonging also increased significantly with active learning, but only for non-URM students. Through structural equation modeling, we demonstrate that, for URM students, the increase in self-efficacy mediated the positive effect of active-learning pedagogy on two metrics of student performance. Our results add to a growing body of research that supports varied and inclusive teaching as one pathway to a diversified STEM workforce.

SUBMITTER: Ballen CJ

PROVIDER: S-EPMC5749958 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.

Ballen Cissy J CJ Wieman Carl C Salehi Shima S Searle Jeremy B JB Zamudio Kelly R KR

CBE life sciences education 20170101 4

Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self-ef ...[more]

PMID: 29054921

Similar Datasets

Project description:Introduction: Active learning is a useful tool to enhance student engagement and support learning in diverse educational situations. We aimed to assess the efficacy of an active learning approach within a large interprofessional first year Medical Cell Biology module taken by six healthcare programmes across the School of Biomedical Sciences at Ulster University, United Kingdom. Materials and methods: An active learning approach was developed for weekly formative assessment using Smartwork to design a weekly interactive multiple-choice quiz to reinforce key concepts specifically for each lecture. We tracked and assessed student performance in the module overall and in each element of course work and exam for 2 years prior to and following the introduction of an active learning strategy to engage and support learning for students from all academic backgrounds and abilities. Results: Full engagement with active learning was significantly associated with an increased overall module performance as well as a significantly increased performance in each element of class test (No engagement vs. Full engagement, p < 0.001), exam (No Engagement vs. Full engagement, p < 0.05) and coursework (No engagement vs. Full engagement, p < 0.001) within this overall total (No Engagement vs. Full engagement, p < 0.01). Partial engagement with active learning was associated significantly improved class test (No engagement vs. partially engaged, p < 0.001) and coursework (No engagement vs. partially engaged, p < 0.05) performance. While a trend toward increased performance in exam and overall module mark was observed, these were not significant. Discussion: Active learning is a useful tool to support student learning across a range of healthcare programmes taken by students with differing backgrounds and academic abilities in an interprofessional and widening participation setting. Student engagement in active learning was highlighted as a key contributory factor to enhanced student performance in all aspects of assessment.

Project description:Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop's original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures.

Project description:In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling 'hook' for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.

Project description:BackgroundMotor learning (ML) science is foundational for physical therapy. However, multiple sources of evidence have indicated a science-practice gap. Clinicians report low self-efficacy with ML concepts and indicate that the lack of access to systematic training is a barrier for practical implementation. The general goal of this preliminary study was to describe the effects of a new educational intervention on physical therapy student's ML self-efficacy and knowledge.MethodsSelf-efficacy was assessed with the Physical Therapists' Perceptions of Motor Learning questionnaire. Data was acquired from third-semester students before their participation in the ML educational intervention. Reference self-efficacy data was also acquired from physical therapy professionals and first and last-semester students. The educational intervention for third-semester students was designed around an established framework to apply ML principles to rehabilitation. A direct experience, the "Learning by Doing" approach, in which students had to choose a motor skill to acquire over 10?weeks, provided the opportunity to apply ML theory to practice in a personally meaningful way. After the intervention self-efficacy was re-tested. ML knowledge was tested with an objective final exam. Content analysis of coursework material was used to determine how students comprehended ML theory and related it to their practical experience. The Kruskal-Wallis and Mann-Whitney U tests were used to compare self-efficacy scores between the four groups. Changes in self-efficacy after the educational intervention were analyzed with the Wilcoxon test. Spearman rank correlation analysis was used to test the association between self-efficacy and final exam grades.ResultsBy the end of the intervention, students' self-efficacy had significantly increased (p?<?0.03), was higher than that of senior students (p?<?0.00) and experienced professionals (p?<?0.00) and correlated with performance on an objective knowledge test (p?<?0.03). Content analysis revealed that students learned to apply the elements of ML-based interventions present in the scientific literature to a real-life, structured ML program tailored to personal objectives.ConclusionsPositive improvements were observed after the intervention. These results need confirmation with a controlled study. Because self-efficacy mediates the clinical application of knowledge and skills, systematic, active training in ML may help reduce the science-practice gap.

Dataset Information

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.

Publications

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets