Technology‐Mediated Tasks: Affordances Considered From the Learners’ Perspectives
Bibliographic record
Abstract
Within the field of instructed second language acquisition, there has been an increase in the amount of research reporting on task‐based language teaching (TBLT; Kim, ; Van den Branden, ). The pervasive use of technology has prompted researchers to examine the potential synergies between TBLT and technology‐mediated teaching (González‐Lloret & Ortega, ; Ziegler, ). The present study examines the perception of 20 learners of English as a second language toward the use of tasks across two modes: a traditional paper‐mediated (PM) information gap task and a technology‐mediated (TM) information gap task. For the PM task, learners used paper resources and worked individually to find information about three colleges, whereas for the technology‐mediated TM task learners turned to the computer to accomplish this procedure. Participants then completed a collaborative information exchange task. To measure their attitudes toward the tasks, the researchers had the learners answer questionnaires following the completion of each task, which included Likert‐type scale items and open‐ended questions. Findings suggest that a majority of the learners benefited from completing a TM information gap task; however, some minor concerns were raised by the learners. Pedagogical implications for a weak form of technology‐mediated TBLT are discussed.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".