Comprehension-Based Learning: The Limits of ‘Do It Yourself’
Bibliographic record
Abstract
In previous publications, the authors reported on the English skills of students who had learned ESL in an experimental comprehension-based program. The performance of grade 4 and 5 students with two or three years of reading and listening was compared to that of students with three years of audio-lingual instruction. On most measures, the students in the comprehension-based program performed as well as or better than the comparison group (Lightbown 1992a; Lightbown & Halter, 1989). In the present paper, the authors report on a follow-up study carried out when students were in grade 8. After six years of an essentially comprehension-based program in ESL, they performed as well as comparison groups of students on measures of comprehension and some measures of oral production but not on measures of written production. This paper includes a description of some particular gaps in the written language of students in the comprehension-based program, includes a follow-up study with secondary school students who had been involved in an experimental program for learning English as a second language (ESL) in primary school, and concludes with a discussion of the need for pedagogical guidance for the development of writing skills.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".