Using activity theory and its principle of contradictions to guide research in educational technology
Bibliographic record
Abstract
<span>This paper describes how activity theory (AT) and its principle of contradictions may be relied on to guide research in educational technology. The paper begins with a theoretical overview of AT and of its principle of contradictions. It follows with a synthesis of studies that have used AT as a lens to study information and communication technologies (ICTs) in educational contexts. We analyse educational technology studies that have focused on contradictions in terms of their underlying assumptions, research questions, approaches to analysis, findings, and implications. The lens of AT and contradictions provides a versatile tool to inquire into various aspects of educational technology use, taking into account individual and institutional perspectives as well as evolution over time. AT and its principle of contradictions provide insights into how transformation may occur with use of ICTs in educational contexts.</span>
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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 | 0.041 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".