THE EFFECTS OF ‘CONCEPT MAPPING ’ ON SECOND LANGUAGE LEARNERS' COMPREHENSION OF INFORMATIVE TEXT
Bibliographic record
Abstract
Abstract. Numerous empirical studies in first language education have highlighted the positive effects of concept mapping as an instructional strategy for text comprehension (Chang et al., 2002). However, in the context of second language, such research has remained limited. The present study aims at observing the effects of an instructional sequence, based on the most effective approaches tested in first languages, on informative text comprehension in French as a second language. Within the framework of this pilot research, the adult French second language learners of an advanced level from the language school of the ‘Université du Québec à Montréal ’ were subject to a weekly intervention over a 4 week period, while a control group equivalent followed the traditional approach (explanation of new words and expressions, discussion of the key concepts). Before reading an informative text, participants in the experimental group were invited to collaboratively replace the labels in a Fill-in Concept Map. Then, after reading the text, the participants were asked to correct it individually. Over the course of the 4 weeks, we used the instructional strategy of the progressive devolution concept map, which is an approach to scaffold fading. The results obtained with comprehension questionnaires on the reading text specific to each meeting indicated that the experimental group obtained a better performance than the group that had used the traditional approach. 1
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".