Student and instructor experiences in the inverted classroom
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
This paper discusses our ongoing experiences with teaching software engineering through an inverted classroom. This course format moves traditional lectures out of in-class hours and into the student's personal study time with prerecorded lectures. We support the inverted classroom with complementary techniques, such as structured discussions, weekly quizzes to ensure students watch the lectures before discussion, an innovative Lego-based workshop, a term project, and guest lectures by industry professionals. The inverted classroom allows the students to have an effective educational experience that encompasses both traditional lectures and an active learning environment. To evaluate the efficacy of this format, we use surveys and interviews of both instructors and students. We examine the time commitment of teaching with this method, from both the instructors' perspective and the students'. We also discuss the time commitment for instructor preparation, and quantitative measures of how the inverted classroom helps smooth the variance in the quality of each instructor's teaching. We also analyze the effectiveness of this technique and our methods for mitigating unintended consequences, such as students having an inexact understanding of the material. Through this evaluation, we distill the effects on student learning and instructor teaching.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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