Some Believe, Not All Achieve: The Role of Active Learning Practices in Anxiety and Academic Self-Efficacy in First-Generation College Students
Bibliographic record
Abstract
First-generation college students face a variety of barriers in higher education compared with their continuing-generation peers. Active learning practices in STEM classrooms can potentially narrow the achievement gap by increasing academic self-efficacy, or confidence in academic abilities. However, these practices can also provoke anxiety in students. Given that anxiety can impair cognitive performance, we sought to understand how first-generation students perceive active learning practices and whether these perceptions affect the anticipated benefits of active learning. As part of a larger study on pedagogical practices in anatomy and physiology courses at the community college level, we asked students to rate various active learning techniques on how much each provoked anxiety and how much each contributed to their learning. All students (N= 186) rated some techniques as more anxiety-provoking than others (e.g., cold calling); however, compared to continuing-generation students, first-generation students’ ratings tended to be higher. First-generation students anticipated doing more poorly in a course and attained lower final grades. Notably, the use of active learning practices did not improve first-generation students’ academic self-efficacy: by the end of term, academic self-efficacy decreased in non-white first-generation students whereas other students showed little change. When introducing active learning strategies, instructors may need to proactively address underrepresented minority students’ emotional reactions and ensure that all students experience success with these practices early in a course as a way to bolster academic self-efficacy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".