Evaluating technology-enhanced learning: A comprehensive framework
Bibliographic record
Abstract
BACKGROUND: The absence of a standard, comprehensive approach to evaluating technology-enhanced learning (TEL) limits the utility of individual evaluations, and impedes the integration and synthesis of results across studies. PURPOSE: To outline a comprehensive framework for approaching TEL evaluation in medical education, and to develop instruments for measuring the perceptions of TEL learners and instructors. METHODS AND RESULTS: Using both theoretical constructs of inquiry in education and a synthesis of existing models and instruments, we outlined a general model for evaluation that links utility, principles, and practices. From this we derived a framework for TEL evaluation that identifies seven data collection activities: needs analysis; documentation of processes, decisions, and final product; usability testing; observation of implementation; assessment of participant experience; assessment of learning outcomes; and evaluation of cost, reusability, and sustainability. We then used existing quality standards and approaches to develop instruments for assessing the experiences of learners and instructors using TEL. CONCLUSIONS: No single evaluation is likely to collect all of this information, nor would any single audience likely find all information elements equally useful. However, consistent use of a common evaluation framework across different courses and institutions would avoid duplication of effort and allow cross-course comparisons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".