Middle School Student Voices on the Function and Utility of a “Learning How to Learn” Course in a Rural Middle School: A Mixed-Methods Study
Bibliographic record
Abstract
The purpose of this study is to explore the voices of high- and low-achieving middle school students around how a student-led assessment (SLA), “learning how to learn” intervention fosters achievement goal orientations and self-efficacy for self-regulated learning (SRL). Participating students included 99 seventh- and eighth-grade students from a rural middle school enrolled in the SLA intervention. This intervention involves teaching students how to become independent learners. Self-report measures were administered and focus groups occurred during the fourth quarter of the academic year. Data analysis revealed no significant differences between high- and low-achieving students regarding their goal orientations, as well as self-efficacy for SRL. During the focus groups, students provided information about the function of the learning how to learn course and voiced the utility of the intervention for improving their study skills. It was also found that, in contrast to high-achieving students, low-achieving students expressed the need for increased support in setting and achieving their learning goals. Findings have implications regarding future refinement and guidelines for implementation of “learning how to learn” SLA courses in middle schools.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".