Flipping from Flipped Classroom to Multimodal Mobile Learning (MML)
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".