Integrating Multimedia ICT Software in Language Curriculum: Students’ Perception, Use, and Effectivenes
Bibliographic record
Abstract
<span>Information and Communication Technologies (ICT) constitute an integral part of the teaching and learning environment in present-day educational institutions and play an increasingly important role in the modern second language classroom. In this study, an online language learning tool Tell Me More (TMM) has been introduced as a supplementary tool in French and German first and second-year language university classes. At the end of the academic year, the students completed a questionnaire exploring their </span><em>TMM</em><span> usage behaviour and perception of the software. The survey also addressed aspects of the respondents' readiness for self-directed language learning. The data were then imported into SPSS and underwent statistical analysis. The results of the study show that 1) relatively few of today's university students are open to the idea of voluntarily using ICT for independent language practice; 2) grade, price, and availability of alternative means of language practice are the most important factors affecting the students' decision to purchase and use ICT software; 3) there is a relationship between the students' decision to buy and use ICT software and their readiness for self-directed learning.</span>
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".