A Learning Management System for Flipped Courses
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
The "flipped classroom" is gaining around in engineering courses. This teaching method has many advantages, such as helping disabled students. However, we observed that many students are less up-to-date than in traditional courses. To counter this problem, we have developed a learning management system (LMS) with unique features oriented for "flipped courses". The new LMS allows students to watch videos, to interact with Jupyter Notebooks and to complete the exercises directly on the website. The LMS automatically creates progression graphics for each student and pushes automatic messages related to their progression. For instructors, the LMS automatically creates statistics about the overall class progression throughout the lessons and exercises and allows targeting students in difficulty whose can then be individually helped. The LMS was introduced in several engineering courses and helped to lower the failure rate. With machine learning algorithms, the LMS can also demonstrate the importance to keep the students continuously up-to-date in a course.
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 | 0.001 | 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