Metacognitive strategies in secondary science education
Bibliographic record
Abstract
Students often struggle to meaningfully reflect on their conceptual understandings, establish well-defined learning goals, and employ strategies that effectively bridge learning gaps. The benefits of metacognitive strategies in the science curriculum to enhance student self-awareness is well-documented in the research literature. Metacognition refers to one's considerations for their own thinking and learning. Metacognitive strategies can be subdivided into three categories: planning, monitoring, and evaluation. Planning strategies are utilized prior to a task or unit to encourage goal setting, establish prior knowledge, and identify learning objectives. Monitoring strategies aid students in actively gauging their learning progress. Evaluation strategies nurture student reflection on learning success and assessment preparation techniques. The effectiveness of planning, monitoring, and evaluation metacognitive strategies on assessment performance and perceived learning was investigated within an Alberta Biology 20 class of 19 students. The project time frame was subdivided into five, approximately two-week sessions, and the first session represented a non-treatment stage. Students implemented planning, monitoring, evaluation, and combined strategies for the subsequent four treatment sessions. At the conclusion of each session, students were summatively assessed on their recent content knowledge. Box and Whisker Plots were generated for a visual comparison of the assessment score distributions. A Friedman Two-Way Analysis of Variance by Ranks and a Post-hoc test examined significance between assessment scores for the five sessions. Assessment score ranks sums were statistically significant between the no treatment sample and treatments 2, 3, and 4 respectively, suggesting that metacognitive strategies may contribute to an increase in assessment performance. Likert-style surveys with accompanying open-ended questions were provided to participants at the conclusion of each treatment. The anonymous surveys required students to compare strategy effectiveness between and within treatments, to consider how likely they were to independently use metacognitive strategies in future classes, and to express their interest in learning additional strategies for a particular type. Survey data supported the claim that the incorporation of metacognitive strategies within the curriculum improved the perception of learning. Most students retained a favorable opinion of metacognition strategies throughout the study, and believed the strategies were effective at fostering the development of conceptual understandings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".