Data for Flipped Classroom Design: Using Student Feedback to Identify the Best Components from Online and Face-to-Face Classes
Bibliographic record
Abstract
Colleges and universities have seen considerable enrollment growth in online courses during the past decade. However, online modalities are not optimal for all subject areas or students. There is growing interest in hybrid, blended, and flipped instruction as a way to incorporate the best of different delivery methods. This study investigates and identifies student preferences for both face-to-face and online learning. Participants were undergraduate students from a mix of freshman, junior, and senior level courses. An open response instrument was used to allow broad insights into students’ responses without biasing or limiting the feedback. Results suggest that the most positive impact with face-to-face learning is interaction through class discussions, group projects and other types of active learning. Females responded more positively than male students to interactivity in face-to-face classes. The data further indicates the most positive impact with online learning experiences is the class structure that supports flexibility, organization, and clear expectations. Nontraditional students reported more positively than traditional students about the benefits of flexible classes with clear course structures. This report should be of interest to educators who wish to take a research-based, student-centric, data-driven approach to the design of flipped or hybrid classrooms.
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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 | 0.028 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".