Impact of evidence-based flipped or active-engagement non-flipped courses on student performance in introductory physics
Bibliographic record
Abstract
We describe the impact of physics education research-based pedagogical techniques in flipped and active-engagement non-flipped courses on student performance on validated conceptual surveys. We compare student performance in courses that make significant use of evidence-based active engagement (EBAE) strategies with courses that primarily use lecture-based (LB) instruction. All courses had large enrollment and often had 100–200 students. The analysis of data for validated conceptual surveys presented here includes data from large numbers of students from two-semester sequences of introductory algebra-based and calculus-based introductory physics courses. The conceptual surveys used to assess student learning in the first and second semester courses were the Force Concept Inventory and the Conceptual Survey of Electricity and Magnetism, respectively. In the research discussed here, the performance of students in EBAE courses at a particular level is compared with LB courses in two situations: (i) the same instructor taught two courses, one of which was a flipped course involving EBAE methods and the other an LB course, while the homework, recitations, and final exams were kept the same; (ii) student performance in all of the EBAE courses taught by different instructors was averaged and compared with LB courses of the same type also averaged over different instructors. In all cases, we find that students in courses that make significant use of active-engagement strategies, on average, outperformed students in courses using primarily LB instruction of the same type on conceptual surveys even though there was no statistically significant difference on the pretest before instruction. We also discuss correlation between the performance on the validated conceptual surveys and the final exam, which typically placed a heavy weight on quantitative problem solving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".