Challenge of Developing and Implementing Multimedia Courseware for a Japanese Language Program
Bibliographic record
Abstract
This paper discusses issues surrounding the development and implementation of Computer-Assisted Language Learning (CALL) at the curriculum- and program-levels. The Japanese program at the University of Alberta has introduced CALL courseware in language courses including those with multiple sections. An evaluation was conducted at the initial implementation stage to measure the success of the project. The results of the evaluation indicated that students and instructors were positive towards the curriculum reform through the implementation of CALL technologies. However, several issues also arose during the integration process. We found that the seamless integration of technologies was difficult to achieve, especially in dealing with a language like Japanese which requires additional software to display and input the idiographic characters. Our experience also underscores the importance of student support in the implementation stage. Special consideration should be taken to achieve a good “fit” between pedagogy and technology. Moreover, each instructor's understanding and sharing of his or her view of the CALL integrated instruction was found to be vital for a program-level CALL implementation. The University of Alberta case serves as an example and benchmark for others planning to conduct a similar project.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".