Teachers, Pupils And Parents’ Perspectives On A Pedagogical Approach To Strengthened Reading Fluency That Uses Touchscreen Computer Tablet To Read-aloud, Recording And Sharing
Bibliographic record
Abstract
Oral reading fluency involves being able to read with accuracy, automaticity, and oral prosody to access to the meaning of texts. Improving pupils’ reading fluency is crucial for the elementary school. Various resources are available to help teachers achieve this quest, in particular educational recommendations and research results that focus on the effects of different forms of interventions. The goal of this paper is to introduce a tool to improve the assimilation of a pedagogical approach created by two experienced second-grade teachers. This approach implies 1) regular appointments with the pupils to set goals and challenges; 2) let the children practice, reading aloud, recording themselves on a touchscreen computer tablet and sharing the recordings with their teacher and parents. After eight months of use, 4 teachers, their 88 pupils and 12 of their parents were invited to participate in interviews in which they were questioned about their experience of the use of the tool and its effects on the students. The analysis shows that the pedagogical approach is reliable and that the participants feel that it has a positive impact on the pupils’ motivation and learning. It allows to identify strengths and weaknesses. This provides arguments for a larger implementation and for further research on its impact.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".