CREATE’ing improvements in first-year students’ science efficacy that are independent of instructor rank and experience in a large, multi-section online introductory course
Bibliographic record
Abstract
Abstract With a primary objective to engage students in the process of science online, we transformed a long-standing laboratory course for first-year science students into a more accessible, immersive experience of current biological research using a narrow and focused set of primary literature and the CREATE pedagogy. The efficacy of the CREATE approach has been demonstrated in a diversity of higher education settings and courses. It is, however, not yet known if CREATE can be successfully implemented online with a large, diverse team of faculty untrained in the CREATE pedagogy. Here, we present the transformation of a large-enrollment, multi-section, multi-instructor course for first-year students in which instructors follow different biological research questions but work together to reach shared goals and outcomes. We assessed students’: (1) science self-efficacy and (2) epistemological beliefs about science throughout an academic year of instruction fully administered online as a result of ongoing threats posed by COVID-19. Our findings demonstrate that novice CREATE instructors with varying levels of teaching experience and ranks can achieve comparable outcomes and improvements in students’ science efficacy in the virtual classroom as a teaching team. This study extends the use of the CREATE strategy to large, team-taught, multi-section courses and shows its utility in the online teaching and learning environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".