The design of effective mobile-enabled tasks for ESP students: A longitudinal study
Bibliographic record
Abstract
This paper describes and reports on the findings of the Enactment Phase of a longitudinal Design Based Research (DBR) study aiming to develop effective design principles for learning materials for English for Special Purpose (ESP) students, enabled by means of mobile devices. The process of data collection and analysis over an eighteen-month period, resulted in a conceptual model and design principles for a mobile-enabled language learning (MELL) solution. The study also generated a broader understanding of the context-embedded nature of ESP learning using mobile devices, specifically the role of aspects of the whole learning environment, ultimately contributing to real-life praxis of the Ecological Constructivist framework and the complementary approach of DBR methodology. This paper focuses on the intervention design and development completed during the Enactment phase (Phase 2). The key outcome of this phase, namely the prototype of the Mobile-Enabled Language Learning Eco-System (MELLES), encompassed eight ESP tasks accessible through a mobile-web portal which served as a gateway to the MELLES network. The design of the MELLES intervention and its constituent tasks are presented.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".