Data for Flipped Classroom Design: Using Student Feedback to Identify the Best Components from Online and Face-to-Face Classes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it