Beyond the technology in Computer Assisted Language Learning: learners’ experiences.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it