<i>What really makes students like a web site? What are the implications for designing web-based language learning sites?</i>
Bibliographic record
Abstract
Faced with reduced numbers choosing to study foreign languages (as in England and Wales), strategies to create and maintain student interest need to be explored. One such strategy is to create ‘taster’ courses in languages, for potential university applicants. The findings presented arise from exploratory research, undertaken to inform the design of a selection of web-based taster courses for less widely taught languages. 687 school students, aged 14-18, were asked to identify a web site that they liked and to state their main reason for liking it. They were invited to include recreational sites and told that their answers could help with web design for the taster courses. To explore the reasons, two focus groups were conducted and student feedback on the developing taster course site was collected. Students nominated search engines and academic sites, sites dedicated to hobbies, enthusiasms, youth culture and shopping. They liked them for their visual attributes, usability, interactivity, support for schoolwork and for their cultural and heritage associations, as well as their content and functionality. They emerged as sensitive readers of web content, visually aware and with clear views on how text should be presented. These findings informed design of the taster course site. They are broadly in line with existing design guidelines but add to our knowledge about school students’ use of the web and about designing web-based learning materials. They may also be relevant to web design at other levels, for example for undergraduates.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".