Beyond the technology in Computer Assisted Language Learning: learners’ experiences.
Bibliographic record
Abstract
The present study is based on a previous pilot study (Gutiérrez-Colon, 2008)[1]. The present study aimed at widening the scope of the pilot study and increased the sample size in number of participants, degree courses and number of universities. This time, four Spanish universities were involved, and the number of participants was 197, who were registered in English Philology (N=72), Business Studies (N=36) and Mechanical Engineering (N=89). The data were organised into four main areas which describe the essential methodological teaching practices that are present and should/should not be avoided in blended virtual courses according to the interviewed students: a) Management of the subject, b) Students’ perception of the subject, c) Design of the course and the documents, d) Feedback from the teacher. The results obtained indicate that techers should modofy their teaching habits and methodology when teaching online. [1] Gutierrez-Colon, M. (2008). Frustration in virtual learning environments. In Handbook of research on e-learning methodologies for language acquisition, (Marriott, R. & Torres, P. Eds). Idea Group Publishing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".