'ICT-Periodism' in the Classroom: Lessons Learnt from English in Action
Bibliographic record
Abstract
This paper illustrates the factors affecting the use of ICT in the classroom in a large scale language development project in Bangladesh. The project, called English in Action (EIA) provides ICT materials along with print based materials for the teachers as well as face-to-face training to bring changes in primary and secondary classroom practices. On-going monitoring data showed that the use of audio materials increases as the intervention progresses through the year, however, the pattern changes when a decrease is observed during the third quarter. The study focused on the factors that have an influence on the use of audio in EIA classrooms during different quarters of the year and explored the underlying causes. The study adopted a mixed method approach and used classroom observations and semi-structured interviews with fifteen primary teachers to gather data. Findings showed that different nature and the pressure of the contents during different quarters of the year and the examination focused education system are the key factors that affect the use of ICT in English classes at the primary level. This raises the concern regarding ‘negative back-wash effect’ (Pan, 2009) of the exam system, which can stop effective pedagogical changes in the classroom from happening.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".