Elaborative Feedback to Enhance Online Second Language Reading Comprehension
Bibliographic record
Abstract
Many higher education students across the world are studying in situations where a high proportion of the academic materials they encounter are online reading texts written in their second language. While the online medium presents a number of challenges to L2 readers, it also enables the provision of a range of support mechanisms, such as instant feedback. This quasi-experimental study investigated the use of two types of feedback, elaborative and knowledge of response, and compares their effectiveness for enhancing online second language reading comprehension. The participants were 113 Emirati students of L2 English at a higher education college in the United Arab Emirates. Data were collected using a pre-test and a post-test measure of reading comprehension and an online reading comprehension exercise containing either elaborative or knowledge of response feedback. The results of the quasi-experiment showed no significant difference between the effects of the two feedback types on comprehension of an online reading text for the sample as a whole. Equally, for the high-proficiency readers, feedback type had no significant effect on text comprehension. However, with regard to the low-proficiency readers, those receiving elaborative feedback performed significantly better on the post-test than those who received knowledge of response feedback. The findings suggest that elaborative feedback can enhance online L2 reading comprehension but that it needs to be tailored specifically to the needs of the L2 readers it is intended to support.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".