CERA 2014: Using HLM to Examine Reading Achievement Using Hierarchy Linear Modeling to Examine Factors Predicting Students' Reading Achievement
Bibliographic record
Abstract
Using Hierarchy Linear Modeling (HLM), this study identified factors, including students ’ language background and exposure for practicing English language, which predict students ’ reading achievement. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) and its accompanying surveys for students and the schools, a twolevel (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 young age, and the amount 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 significant variables is needed in order for a full picture to be seen. This research shed lights for educators regarding how students ' language history, and their amount of exposure to practice English contribute to predicting students ’ reading achievement, thus helping English learning students and students of poor reading skills. 1 CERA 2014: Using HLM to Examine Reading Achievement
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".