Cognitive Modelling: A Case Study of Reading-to-write Strategy Instruction and the Development of Second Language Writing Expertise in a University English for Academic Purposes Writing Course
Bibliographic record
Abstract
This case study investigates how the teaching of cognitive strategies in an English for Academic Purposes (EAP) writing course at a Canadian university occurs and relates to students' engagement in course writing tasks. While universities offer writing courses in order to improve academic writing skills, English Language Learners (ELL) may still be considered poor writers by mainstream standards. Perhaps the problem is not writing skill, but an ineffective linking of reading and writing strategic knowledge to the task at hand. Pedagogically interconnecting reading and writing skills reciprocally supports learner proficiency development, which may be enhanced by directly teaching awareness raising strategies. This doctoral study seeks to investigate the declarative, procedural and conditional knowledge of what a teacher actually does in the classroom. This instrumental qualitative case study took place in a 10-week EAP writing course. Data collection involved classroom observation, open-ended interviews, reflective questionnaires and retrospective think-aloud tasks of 6 focal students. Analysis of the classroom observations, stimulated recalls, interviews and end-of-term questionnaires were thematically analysed for links between observed teaching activities and student perceived awareness of learning. Taxonomies of teaching activities and learning activities incorporated five aspects of reading and writing behaviours. Teaching episodes are being analysed in one of three ways: Episodes of Raising Awareness (ERA), where teachers may only raise cognitive awareness by mentioning strategies for learning; Episodes of Strategy Explanation (ESE), where teachers explain and demonstrate the strategy; and Episodes of Cognitive Modelling (ECM), which entails the teacher verbalising by thinking aloud and demonstrating strategies as the expert. Student stimulated recalls were analysed as Episodes of Cognitive Learning (ECL). Analyses of the observations and the stimulated student recalls reveal results that link explicit teacher task modelling (ESEs and ECMs) by using think-alouds and demonstration to learner task awareness and strategy implementation. This study provides insights for researchers and educators across disciplines on the importance of the relationship between actively teaching cognitive strategies and how learners problem-solve or apply learning.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".