Students’ perspectives and experiences with equity, diversity, inclusion, and accessibility in online and in-person undergraduate science laboratory courses
Bibliographic record
Abstract
Inclusive education strengthens the capacity of academic systems and addresses the needs of all learners. Although colleges and universities are embracing diversity and introducing inclusive principles into higher education policies and practices, there is scarcity in research exploring undergraduate university students’ participation and engagement in science laboratories, especially among underrepresented and equity-deserving groups. Accordingly, this study explores students’ perspectives on undergraduate laboratory courses and investigates best practices for creating equitable and accessible laboratory environments, both in-person and online. This study addresses the following research questions: 1) How accessible are online and in-person undergraduate laboratory courses to students? 2) What equity, diversity, inclusion, and accessibility (EDIA) barriers exist in laboratory courses? and 3) What support structures are recommended to ensure inclusion of all students in laboratory courses? A mixed methods design was employed to gather data using an online questionnaire and semi-structured interviews with 58 students in undergraduate laboratory science courses from diverse cultural, ethnic, racial, and socioeconomic backgrounds at a Canadian university. Findings highlight that students considered online labs to be flexible, convenient, self-paced, relaxed, and inclusive with respect to aspects involving physical disabilities and language barriers. In-person labs were deemed fast-paced, yet valuable for technical skills and inclusive considering elements pertaining to collaboration, academic support, and communication with peers and faculty members. Students highlighted the following as common inclusion barriers in both online and in-person delivery formats: accessibility (physical and language), financial, and stereotypical barriers. Recommendations for improving lab accessibility were also included. This research has significant implications for the design of laboratory courses and other experiential learning environments in higher education, particularly considering recent transitions and modifications in education. This paper will discuss implications related to the following sub-themes in the special issue: instructors’ professional development, digital education, and quality enhancement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".