ICT in Language Learning - Benefits and Methodological Implications
Bibliographic record
Abstract
ICT as a medium for teaching is becoming more and more acknowledged. In this article we wish to share some aspects of using ICT that have proved positive and stimulating both for students and the teacher. We share our experience in using the Blackboard e-learning environment for teaching language courses in English and Swedish (different levels), for learning terminology, and ESP (English for Specific Purposes). Our focus will be on how the web-based environment can be used for supporting student-centred learning, increasing student motivation, individualisation and cooperation in creating the study-materials, at the same time developing a feeling of “us” and of belonging together. Taking a look at our different past and current courses, we will view different ways of motivating students by engaging them in building the learning materials: data-bases on specific research topics, power-point presentations and on-line dictionaries. We analyse how the ICT solutions can be used as a support for different classroom activities, group-work and pair-work assignments; for independent work; for enforcing student-centred learning and the principles of individualisation; forming one´s personal opinion, and being able to express it on topical issues.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".