Flipping from Flipped Classroom to Multimodal Mobile Learning (MML)
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
Selecting the right training and the right strategy, with the diversification of media and methods, are great challenge for all teaching professionals. Reverse Pedagogy using flipped classroom is a teaching strategy based on a mode where the lecture part of the course is indirectly assigned to students in the form of homework, team projects, video listening or reports to do before meeting the classroom teacher. We have initiated a pioneering work in developing the flipped classroom approach in science and engineering integrating remote laboratory work strategy. In our model, students go through different modes. The proposed Multimode Mobile Learning (MML) model allows students to go through a multitude of modes to enhance their learning. They go from Problem Based Learning (PBL) mode to asynchronous and synchronous distance learning modes by performing team based remote laboratory. The use of mobile Information and Communications Technology (ICT) solutions has led us to describe our model as a Multimode Mobile Learning (MML) model. This innovative learning approach has been introduced in three different Quebec universities having specific context for each institution. Promising results have been obtained showing that the proposed MML model has a wide range of attributes allowing to enhance students learning interests and skills.
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 | 0.004 | 0.005 |
| 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 | 0.001 |
| 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