SUCCESSFUL IT PEDAGOGICAL INTEGRATION: HAVING A LOOK AT THE WHOLE PICTURE
Bibliographic record
Abstract
The ARC (Association pour la recherche au collegial) carried out a meta-analysis that helped identify factors that must be considered for the successful integration of IT into teaching that is, factors that would have positive impacts on student success. The following learning devices proved effective: 1) devices that rely on specialized adaptive drill and practice tools; 2) devices that promote meta cognition; or 3) devices that support collaborative learning. The meta-analysis also indicated that all three categories remain sensitive to the influence of conditions linked to the organizational environment, such as the users proficiency level in using IT (teacher and student training), equipment (material, software), appropriateness/availability of support, and changes in practices for professionals and administrators (pedagogical management and institutional policy). In order to improve this first heuristic model of successful IT integration into teaching, another question had to be considered: how do the professionals’ patterns of action compare to those in the ARC meta analysis model? To answer this question, the second part of the ARC study was conducted. Four college network experts (in pedagogical counselling and research) were interviewed on what they considered as the determining factors for successful IT integration in the classroom. Using a specific, pre-identified series of verbs, conceptual maps were created to reproduce and analyse the data gathered from the interviews with the four experts. All four conceptual maps were validated with their authors. The maps were compared to each other as well as to the meta-synthesis model. The result was an enriched heuristic model designed to explain successful IT integration at the collegial level. Within the framework of this presentation, our objectives are: 1) to present the methodology that led to our heuristic model; 2) to discuss our model in the light of the factors that were found to be important for successful IT integration into teaching/learning; and 3) to report on the progress of the work in the third part of the study.
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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 | 0.210 | 0.309 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".