ICT in Language Learning - Benefits and Methodological Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it