Shima Salehi is a Research Assistant Professor at Stanford Graduate School of Education, and the director of IDEAL research lab, the research component of Stanford IDEAL initiative to promote inclusivity, diversity, equity and access in learning communities. Her research focuses on how to use different instructional practices to teach science and engineering more effectively and inclusively. For effective science and engineering education, Dr. Salehi has studied effective scientific problem-solving and developed empirical framework for main problem-solving practices to train students in. Based on these findings, she has designed instructional activities to provide students with explicit opportunities to learn these problem-solving practices. These activities have been implemented in different science and engineering courses. For Inclusive science and engineering, she examines different barriers for equity in STEM education and through what instructional and/or institutional changes they can be addressed. Her recent works focus on what are the underlying mechanisms for demographic performance gaps in STEM college education, and what instructional practices better serve students from different demographic backgrounds. Salehi holds a PhD in Learning Sciences and a PhD minor in Psychology from Stanford University, and received a B.Sc. degree in Electrical Engineering from Sharif University of Technology, Iran. She is the founder of KhanAcademyFarsi, a non-profit educational organization which has provided service to Farsi-speaking students, particularly in under-privileged areas.
Assessment, Testing and Measurement
Brain and Learning Sciences
Equity in Education
Technology and Education
- Inclusive Instructional Practices: Course Design, Implementation, and Discourse FRONTIERS IN EDUCATION 2021; 6
Mediation Analysis in Discipline-Based Education Research Using Structural Equation Modeling: Beyond "What Works" to Understand How It Works, and for Whom.
Journal of microbiology & biology education
2021; 22 (2)
Advancing the field of discipline-based education research (DBER) requires developing theories based on outcomes that integrate across multiple methodologies. Here, we describe mediation analysis with structural equation modeling as one statistical tool that allows us to further examine mechanisms underlying well-documented trends in higher education. The use of mediation analysis in educational settings is particularly powerful, as learning outcomes result from complex relationships among many variables. We illustrate how mediation analysis can enhance education research, addressing questions that cannot be easily reached otherwise. We walk through critical steps to guide decision-making in mediation analysis and apply them to questions using real data to examine performance gaps in large introductory courses in biology. Through the use of mediation analysis with structural equation modeling, we add to a growing body of research that shows diverse quantitative approaches support evidence-based teaching in higher education.
View details for DOI 10.1128/jmbe.00108-21
View details for PubMedID 34594447
Mixed results from a multiple regression analysis of supplemental instruction courses in introductory physics.
2021; 16 (4): e0249086
Providing less prepared students with supplemental instruction (SI) in introductory STEM courses has long been used as a model in math, chemistry, and biology education to improve student performance, but this model has received little attention in physics education research. We analyzed the course performance of students enrolled in SI courses for introductory mechanics and electricity and magnetism (E&M) at Stanford University compared with those not enrolled in the SI courses over a two-year period. We calculated the benefit of the SI course using multiple linear regression to control for students' level of high school physics and math preparation. We found that the SI course had a significant positive effect on student performance in E&M, but that an SI course with a nearly identical format had no effect on student performance in mechanics. We explored several different potential explanations for why this might be the case and were unable to find any that could explain this difference. This suggests that there are complexities in the design of SI courses that are not fully understood or captured by existing theories as to how they work.
View details for DOI 10.1371/journal.pone.0249086
View details for PubMedID 33793607
- Variation in Incoming Academic Preparation: Consequences for Minority and First-Generation Students FRONTIERS IN EDUCATION 2020; 5
- Variation in Incoming Academic Preparation: Consequences for Minority and First-Generation Students Front. Educ. 2020; 5 (552364)
- Can Majoring in Computer Science Improve General Problem-solving Skills? SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education 2020: 156–161
- Demographic gaps or preparation gaps?: The large impact of incoming preparation on performance of students in introductory physics PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH 2019; 15 (2)
Exploring bias in mechanical engineering students' perceptions of classmates.
2019; 14 (3): e0212477
Gender disparity in science, technology, engineering, and math (STEM) fields is an on-going challenge. Gender bias is one of the possible mechanisms leading to such disparities and has been extensively studied. Previous work showed that there was a gender bias in how students perceived the competence of their peers in undergraduate biology courses. We examined whether there was a similar gender bias in a mechanical engineering course. We conducted the study in two offerings of the course, which used different instructional practices. We found no gender bias in peer perceptions of competence in either of the offerings. However, we did see that the offerings' different instructional practices affected aspects of classroom climate, including: the number of peers who were perceived to be particularly knowledgeable, the richness of the associated network of connections between students, students' familiarity with each other, and their perceptions about the course environment. These results suggest that negative bias against female students in peer perception is not universal, either across institutions or across STEM fields, and that instructional methods may have an impact on classroom climate.
View details for PubMedID 30845229
- The impact of incoming preparation and demographics on performance in Physics I: a multi-institution comparison arXiv:1905.00389 [physics.ed-ph] 2019
- Tools for Science Inquiry Learning: Tool Affordances, Experimentation Strategies, and Conceptual Understanding JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY 2018; 27 (3): 215–35
- Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning CBE-LIFE SCIENCES EDUCATION 2017; 16 (4)
Exams disadvantage women in introductory biology
2017; 12 (10): e0186419
The gender gap in STEM fields has prompted a great deal of discussion, but what factors underlie performance deficits remain poorly understood. We show that female students underperformed on exams compared to their male counterparts across ten large introductory biology course sections in fall 2016 (N > 1500 students). Females also reported higher levels of test anxiety and course-relevant science interest. Results from mediation analyses revealed an intriguing pattern: for female students only, and regardless of their academic standing, test anxiety negatively impacted exam performance, while interest in the course-specific science topics increased exam performance. Thus, instructors seeking equitable classrooms can aim to decrease test anxiety and increase student interest in science course content. We provide strategies for mitigating test anxiety and suggestions for alignment of course content with student interest, with the hope of successfully reimagining the STEM pathway as one that is equally accessible to all.
View details for PubMedID 29049334
Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.
CBE life sciences education
2017; 16 (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-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.
View details for DOI 10.1187/cbe.16-12-0344
View details for PubMedID 29054921
View details for PubMedCentralID PMC5749958