Agency in Educational Technology: Interdisciplinary Perspectives and Implications for Learning Design
Bibliographic record
Abstract
Abstract Advancing learners’ agency is a key educational goal. The advent of personalized EdTech, which automatically tailor learning environments to individual learners, gives renewed relevance to the topic. EdTech researchers and practitioners are confronted with the same basic question: What is the right amount of agency to give to learners during their interactions with EdTech? This question is even more relevant for younger learners. Our aim in this paper is twofold: First, we outline and synthesize the ways in which agency is conceptualized in three key learning disciplines (philosophy, education, and psychology). We show that there are different types and levels of agency and various prerequisites for the effective exercise of agency and that these undergo developmental change. Second, we provide guiding principles for how agency can be designed for in EdTech for children. We propose an agency personalization loop in which the level of agency provided by the EdTech is assigned in an adaptive manner to strike a balance between allowing children to freely choose learning content and assigning optimal content to them. Finally, we highlight some examples from practice.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.060 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".