A Web-based Framework for the Evaluation of End-User Experience in Adaptive and Personalised eLearning Systems
Bibliographic record
Abstract
The evaluation of interactive adaptive and personalised systems has long been acknowledged as a difficult, complicated and very demanding endeavour due to the complex nature of these systems. This paper describes a web-based framework for the evaluation of end-user experience in adaptive and personalised e-Learning systems. The benefits of the framework include: i) the provision of an interactive reference and recommendation tool to encourage the evaluation of systems that fulfil certain methodological requirements; ii) the collaborative nature of the framework facilitates the sharing of information among researchers from the information technology, adaptive hypermedia, information retrieval and e-Learning communities; iii) the identification of pitfalls in the evaluation planning process as well as in data analysis; and iv) the translation of presented information into users language of choice. This paper also presents a review of User-Centred Evaluation approaches, methodologies and techniques adopted by current systems and frameworks. The results of this review are analysed. From these results, an architectural design for the framework was specified.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".