A comparison of flipped learning with traditional learning (face-to-face) in large calculus courses: The effects on students’ achievement and cognitive engagement
Bibliographic record
Abstract
This research paper investigates the effectiveness of combined flipped classroom (FC) with a plethora of Prep materials, small-group Collaboration, student Presentations, TopHat Clickers, and Engaged labs (PCPCE) on students’ achievement and cognitive engagement from the students’ perceptions. Although FC format is not new, we use a different implementation of an FC (FC-PCPCE) in a calculus class. Educational and edutainment elements were investigated through a questionnaire that assessed learning gain, relatedness, challenges, learner-related factors, and self-reflection in terms of mathematics ability and perceived interest in the subject. We analyze both qualitative and quantitative survey responses from 354 first-year students participating in calculus classes at a large Canadian public university. We compare the perceptions of FC-PCPCE students to those of students in a traditional (i.e., non-flipped) classroom. The survey analysis shows that even with many students enjoying the implementation of FC-PCPCE format, students in the traditional classroom reported higher levels of satisfaction, interest, belonging, content recall, and experienced fewer academic challenges such as procrastination. The results of this study will aid educators in designing courses that benefit students and guide researchers who wish to pursue further studies on this topic.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".