The Move is On! From the Passive Multimedia Learner to the Engaged Co-creator
Bibliographic record
Abstract
The educational integration of information and communication technologies (ICTs) has led to unfounded hopes of meeting many recurring educational challenges: from increasing learner motivation to lowering drop-out rates. ICTs are not an educational revolution per se; in some situations, their pedagogical usage lead to truly technologically-enhanced learning (TEL) situations, whereas in others, ICTs could relegate the learner to a passive spectator or low-interactivity user/consumer of multimedia content that limits the implementation of a socio-constructivist learning process based on a collaborative knowledge construction process. In this article, we analyze the limits of techno-centric approaches in the integration process of ICTs to teaching and learning, and argue for active learning and reflexive approaches to TEL. The Passive-Participatory (P-P) model we are suggesting can be termed as being socio-constructivist, participatory and inclusive as it allows teachers to integrate ICTs into their own specific educational context. Our model introduces five learning engagement levels in the pedagogical usage of technology: (Level 1) passive ICT usage, (Level 2) interactive ICT usage, (Level 3) content creation, (Level 4) content co-creation and, ultimately, (Level 5) participatory knowledge co-creation, which is oriented toward problem understanding within learning/knowledge-building communities. Building on Coates' definition of learning engagement as being the extent to which learners are actively involved in educational activities, the PP model of TEL activities stresses learning engagement could be limited to passive listening (e.g. video), low-interactivity usages (e.g. interactive school manuals), or could be supported through the usage of technologies for engagement in creative work.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".