Championing the Involvement of Practitioners in the Biochemistry Educational Research Process: A Phenomenological View of the Early Stages of Collaborative Action Research
Bibliographic record
Abstract
The disparity between post-secondary STEM instruction and the practices suggested in education and cognitive research is not a novel issue. Despite evidence-based practices being available to practitioners, traditional lecture-based instruction continues to dominate higher STEM education. In this study, we discussed practitioner involvement in biochemistry education research as a potential means to address the gap between research and practice. We used phenomenology as a lens through which to view faculty experiences of participating in a team-based curricular redesign. We administered a concept inventory to examine undergraduate students’ understanding of key concepts and to identify misconceptions. We captured faculty perspectives and reflections on student data through semi-structured interviews, finding that faculty dissatisfaction with traditional practices were rooted in experiences from early on in their teaching careers. Their students demonstrated a lack of conceptual understanding, similar to findings of other studies in undergraduate biochemistry, and key misconceptions the student population held were identified. When examining students’ conceptual understanding data, the faculty gained new insights into where students struggle in the course that they would not have gained without participation in this project. This reinforced their desire to implement curricular change. These findings add to the available data on students’ conceptual understanding in biochemistry and suggest that shared assessments like concept inventories can unify instructors as they engage in team-based curricular reform.
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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 | 0.085 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".