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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".