Blending classroom instruction with online homework: A study of student perceptions of computer-assisted L2 learning
Bibliographic record
Abstract
Abstract This study investigates the impact of an online workbook on the attitudes of 245 second language (L2) Spanish learners toward this pedagogical tool over two consecutive semesters. The treatment consisted of four hours of classroom instruction and one set of online homework per week, during two consecutive semesters. Students' attitudes toward the electronic workbook were measured by means of a survey administered after eight months of exposure to the workbook. The qualitative data of the survey was compared to quantitative data from two different language assessment tests. The results of these tests indicated a significant increase in grammar scores. These results are consonant with the positive findings of student perceptions about the online workbook obtained in this and previous studies, emphasizing its benefits in terms of accessibility to the material, user-friendliness, and instant error feedback. More importantly, most students praised the usefulness of the online workbook for language learning, particularly in the areas of grammar and vocabulary acquisition. Despite participants' mostly positive attitudes, the survey also revealed some negative aspects of the use of the online workbook, such as the amount of time needed to complete the online exercises. This paper addresses these issues, and provides suggestions to overcome this type of problem.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".