USING MOBILE-BASED FORMATIVE ASSESSMENT IN ESL/EFL SPEAKING
Bibliographic record
Abstract
With the widespread application of smartphones in and outside the classroom, mobile-based teaching and learning is drawing much attention and hence being extensively practised nowadays across the globe. Recently, using smartphones for assessment purposes has been a new phenomenon and the researchers are still examining what processes the use of mobile-based assessment tools may include and what outcomes and challenges they can cause to teachers and students in terms of learning/teaching performance, motivation and attitudes. There have been a good number of research studies on the use of Mobile Assisted Language Learning (MALL) or Mobile Learning (ML) in EFL or ESL classroom but not much literature is known about the mobile-based language assessment, especially mobile-based formative assessment (MBFA). Hence, this study attempts to shed light on MBFA and review the recent literature available on it and its effective utilization in developing ESL/EFL speaking skill. This paper uses a qualitative research method that exclusively uses the relevant secondary references/works available on the topic. The literature revealed that MBFA practices in ESL/EFL speaking classes are effective to a certain extent and some tools and procedures seem to be more effective than others depending on the design principles and strategies used by teachers or app developers.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".