2002 Pan-Canadian Education Research Agenda Symposium
Bibliographic record
Abstract
The opinions expressed in this paper are those of the authors and do not represent the views of the Canadian Education Statistics Council The literature devoted to technology and education is replete with claims regarding the contribution of computer technologies to teaching and learning in elementary and secondary schools. The claims have fuelled expectations and encouraged local and provincial school jurisdictions to expend significant resources on new technologies. This review of research is a response to the call by the Canadian Education Statistics Council to prepare a paper addressing some dimension of the impact of information and communication technology (ICT) on teaching and learning in elementary, secondary, or post secondary education in Canada. We have focussed our review on two major areas: 1) the efficacy of ICTs for achievement, motivation, and metacognitive learning; and 2) the impact of ICTs on instruction in content areas in elementary and secondary schools. Mindful of the importance of contextual factors such as ethno-cultural and linguistic diversity, we have attempted to focus on Canadian research. Nevertheless, because the material devoted to Canada is scarce, we have augmented our review with international research on these topics. It was our intention to identify claims that would enable those responsible for the formation or implementation of policy to make informed decisions. Few, if any, claims were sufficiently well researched or well evidenced to provide direction for policy. We conclude our review with a discussion of policy, the impact of research in the classroom, and suggestions of further research.
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How this classification was reachedexpand
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 | 0.003 |
| 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) | 0.030 | 0.004 |
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; both teacher heads agree on what is shown here.
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".