An Investigation into Factors that Differentiate Reading Comprehension Skill Profiles of Young Adolescent Students
Bibliographic record
Abstract
Provincial literacy scores in Ontario provide results of academic achievement but little is known about achievement gaps between groups of students. This study focused on better understanding studentsâ reading comprehension skill profiles based on whether they met the provincial standard or not using data from a provincial reading achievement assessment. The study examined the differences in reading skill mastery among Grade 6 students, using Cognitive Diagnostic Modeling (CDM) that provided a much finer grained representation of studentsâ reading comprehension skills than traditional aggregated-test scoring (Jang, 2009). The responses of 122,269 Grade 6 Ontario students who wrote the 2012 English version of the provincially mandated reading achievement test developed by EQAO in 2012 were considered. This study examined differences in reading profiles among struggling adolescent readers based on their status of either Learning Disability (LD), participation within an English as a Second Language/English Literacy Development (ESL/ELD) program, as well as having both an LD and participation in a ESL/ELD program. Based on the findings, this study discusses how achievement groups show different patterns of mastery. Students who met or exceeded the provincial standard showed a strength in implicit understanding of text whereas struggling readers, that is, those who did not meet the provincial standard, most often mastered vocabulary, explicit understanding, and inference making over implicit understanding and making inferences. Overall, students most often mastered the skills of understanding vocabulary, explicit understanding, and making inferences over implicit understanding and textual organization. However, when studentsâ skill mastery profiles were considered according to LD, ESL/ELD, and a combined LD-ESL/ELD status, distinctive patterns of mastery were not evident as had been hypothesized. Put together, students achieving a Level 1 and Level 2 and students achieving a Level 3 and Level 4 showed similar skill master profiles, regardless of their status. The study further discusses how intervention focusing on building implicit understanding of text may help struggling readers increase their reading comprehension.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".