The effect of CALL on L2 grammar and vocabulary learning. Students´ perfomance and perceptions
Bibliographic record
Abstract
Previous studies have investigated the use of Computer-Assisted Language Learning (CALL) in English as a Foreign Language (EFL) teaching. However, there is a lacuna in research exploring its effectiveness in Secondary Education settings regarding grammar and vocabulary teaching. This study is aimed to compare and analyse the effectiveness of computer-based instruction (CBI) and textbook-based instruction as well as the students’ perceptions for the two areas. The participants were 11 secondary students of intermediate English proficiency level who received both methods of instruction when working on grammar and vocabulary. Quantitative and qualitative data were gathered using two grammar and vocabulary post-tests and a questionnaire. Results in performance showed that improvement was statistically significantly higher when CBI was employed which was in alignment with the students’ preference for online materials as they were considered more motivating than textbook-based ones.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".