Assessment of Optimal Pedagogical Factors for Canadian ESL Learners’ Reading Literacy Through Artificial Intelligence Algorithms
Bibliographic record
Abstract
The current study explored the effective pedagogical factors that distinguish high-achieving from low-achieving ESL (English as a second language) primary school learners in reading literacy in Canada. In total, 203 samples (167 high-achieving learners and 36 low-achieving learners from 128 primary schools) in the 4th grade were drawn from the public database of Progress in International Reading Literacy Study (PIRLS) 2016, which is the benchmark for large-scale assessments of reading literacy targeting fourth-grade students. For the first time in the ESL-related research, this study applied an artificial intelligence approach, support vector machine (SVM), to concurrently analyze 41 pedagogical factors associated with reading materials, classroom organization, reading strategies, in-class reading activities and post-reading activities. The overall 41 factors collectively distinguished the high-achieving readers from the low-achieving readers with a high accuracy score (0.793) via SVM. Then, these 41 factors were ranked according to their contribution to the SVM model through SVM-based recursive feature elimination (SVM-RFE). Eventually, an optimal factor set was selected by the SVM-RFE cross validation, which contained 10 effective pedagogical factors centered on reading materials, reading strategies and in-class reading activities for fourth-grade high-achieving ESL learners in reading literacy. Suggestions based on solid data analysis would facilitate infrastructural and pedagogical improvements in ESL reading education.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".