Foreign Language Teacher's Attitudes Towards a Pre-designed Language Learning System
Bibliographic record
Abstract
Once the pandemic concluded, the Foreign Languages Department of a Chilean state university hired a Canadian company to implement a pre-designed language learning system (PLLS). This platform was to be used by all teachers and students, as it contained various activities to develop all four language skills, including pronunciation practice through AI-based voice recognition. This study explores the attitudes of 17 university teachers towards using these pre-elaborated resources, activities, and assessments in their communicative English and German courses. A mixed-method approach was used, involving a survey based on the Technology Adoption Model (TAM) and individual interviews. Descriptive statistics were obtained from the survey responses, and qualitative data were analysed using content analysis techniques. The results indicate that teachers' attitudes towards the PLLS were generally neutral to negative. Instructors expressed their concerns about the system's pre-designed content and perceived functionality. Perceived ease of use and usefulness were rated low, reporting difficulties in navigation and alignment with their teaching styles. Perceived enjoyment received the lowest rating, mentioning issues such as disconnected content and lack of progressive structure. Qualitative data revealed technical problems, increased workload, and concerns about the system's impact on student motivation and learning outcomes. While some positive aspects were noted, the overall attitude towards the PLLS was predominantly negative, highlighting the need for better alignment with pedagogical goals and improved implementation strategies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".