Using Hierarchical Linear Modelling to Examine Factors Predicting English Language Students’ Reading Achievement
Bibliographic record
Abstract
Using Hierarchical Linear Modelling (HLM), this study aimed to identify factors such as ESL/ELL/EAL status that would predict students’ reading performance in an English language arts exam taken across Canada. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) along with the accompanying surveys for students and the schools, a two-level (student level and school level) HLM model was analyzed for predictive relationships. Results showed that, at the student level, predictors such as students’ participation in class discussions, language spoken at home, parents’ encouragement to read at a young age, and the number of individual projects requiring students to work outside of class contributed significantly to the students’ reading scores. However, none of the school-level predictors were found to be significant. All the significant predictors contributed to only 12% of the variability in this HLM model. Identification of more signi cant variables is needed in order to have a full picture of students’ reading competence and achievement. S’appuyant sur la modélisation linéaire hiérarchique (MLH), ce e étude porte sur l’identi cation des facteurs, comme le statut ALS/ELL/ALA, qui prédiraient les acquis en lecture d’élèves lors d’un examen d’anglais administré partout au Canada. Les auteures ont employé des données du Programme pancanadien d’évaluation (PPCE), y compris les sondages connexes pour les élèves et les écoles, a n d’analyser les liens prédictifs d’un modèle HLM à deux niveaux (élève et école). Les résultats indiquent que les prédicteurs tels la participation des élèves aux dis- cussions en classe, la langue parlée à la maison, la mesure dans laquelle les parents encouragent leurs enfants à lire dès un jeune âge et le nombre de projets individuels exigeant du travail à l’extérieur de la salle de classe, contribuaient de façon significative aux résultats des élèves en lecture. Toutefois, aucun des prédicteurs au niveau de l’école ne s’est révélé comme étant significatif. Dans leur ensemble, les prédicteurs significatifs n’ont contribué qu’à 12% de la variabilité du modèle MLH. A n d’arriver à une vue globale du rendement et de la compétence en lecture des élèves, il faudra identifier plus de variables signi catifs.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".