Using Repeated- Reading and Listening –While- Reading via Text-To- Speech APPs. in Developing Fluency and Comprehension
Bibliographic record
Abstract
One of the challenges in teaching a foreign language is: finding appropriate ways to enable students to develop their reading fluency and comprehension. Repeated reading and listening-while-reading are two significant strategies that enhance students’ fluency and comprehension. This study aimed to develop fluency and comprehension of EFL college students. During the treatment, the teacher trained the students to use some free Text to Speech apps that support oral repeated reading RR and listening while reading LWR activities. Pre-post tests were used to assess students’ reading fluency and comprehension. Data obtained from the tests were analyzed statistically through SPSS software. Results indicated development in students’ reading fluency and comprehension. Conclusions suggested the use of RR and LWR through Text to speech apps to assist the reading skills of higher education students. It is recommended that TTS Apps are promising tools that can be integrated into reading instruction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".