USING MOBILE-BASED FORMATIVE ASSESSMENT IN ESL/EFL SPEAKING
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
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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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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