A Seamless Learning Design for Mobile Assisted Language Learning: An Iranian Context
Bibliographic record
Abstract
Recent developments in information communication technology (ICT) have resulted in a paradigm shift in e-Learning and there is a growing interest in developing design-based research (DBR) focusing on learners and their involvement in knowledge sharing in a contextualized mode. The present study reports a mobile-assisted language learning (MALL) design with a focus on contextualized student-created content having a seamless learning approach. The students in this study (N= 24) used their mobile devices to take photos and create artifacts to represent English idioms and share them on Padlets with their peers for further discussion and feedback. In the first four weeks of the study, students were taught English idioms and in the following next two weeks they created and shared their own artifacts to represent the learnt idioms. The post-study reflections and results of the interviews and obtained from students and the teacher at the end of study revealed that they favor and support greater learner autonomy achieved by learner-generated context (LGC) which bridges the in-classroom and out-of-classroom learning. The article also highlights the necessity of reconceptualization of teachers and students’ perceptions of mobile use in language learning in Iran.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".