The conceptualisation of cognitive tools in learning and technology: A review
Bibliographic record
Abstract
The term cognitive tool has been used in many areas of academic specialisation, where it has taken on multiple connotations. In this historical and systematic review, we investigate the conceptualisation of cognitive tools in the learning sciences and educational technology. First, the theory of cognitive tools vis-à-vis learning and development is traced from Vygotsky and Soviet psychology through to its use in current educational technology and learning design. Second, we present a systematic review of cognitive tools in peer-reviewed research literature. We found the term cognitive tool was often used vaguely or with extreme generality. When used more specifically, it referred to communication methods such as visualisations, metaphors, symbols, and hypermedia; or interactive interfaces and environments such as templates, databases, simulations, games, and collaborative media. We offer a definition of software-based cognitive tools founded on the attributes of representation, interactivity, and distributed cognition, which commonly feature in the work of influential theorists; and we explain implications of the definition for designing, evaluating, and researching learning technologies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".