Assessing Matriculation College Students’ Metacognitive Awareness Reading Strategies (MARS) in Biology
Bibliographic record
Abstract
This study examines the relationship between student’s perceived use of Metacognitive Awareness Reading Strategies (MARS) in reading Biology books and corresponding Biology achievement. This study also identified the effective reading strategies that affect students’ Biology achievement in a particular semester. This study selected 318 Biology students by random sampling which comprised 97 (30%) male and 221 (70%) female students who were studying in one of the Matriculation Colleges in Kedah state, Malaysia. This study use Metacognitive Awareness of Reading Strategies Inventory (MARSI) constructed by Mokhtari and Reichard (2002). Findings of this study show that there is a positive weak relationship between perceived use of MARS and their Biology performance in Matriculation Programme. This study also show that Global Reading Strategies and Problem-Solving Strategies are predictors but Support Reading Strategies is not a predictor of Biology achievement. In investigating the effects of strategies, Global Reading is the best contributor followed by Problem-Solving Strategies to predict Biology achievement. It is suggested that teachers learn how to practice reading strategy instruction in the classroom. Students themselves should learn MARS and apply them in their reading and focus more on Global Reading Strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".