STRATEGIES FOR ACADEMIC SUCCESS: AN EARLY INTERVENTION APPROACH FOR BUILDING METACOGNITIVE SKILLS IN FIRST-YEAR UNDERGRADUATE STUDENTS
Bibliographic record
Abstract
Strategies for Academic Success is a co-curricular workshop for first-year undergraduates on metacognitive skills and learning strategies that aims to support students’ achievement of their learning goals. After multiple iterations, self-reported data has been collected, which allows us to examine and reflect on the learning strategies and habits that students have put into practice as a result of participating in the session, as well as whether the timing of session plays a role in determining the impacts of the content of study habits in students. In sum, we have found that certain strategies resonate more strongly with students based on whether they are entering university or have had at least one semester of university learning experience. Whereas there are broad applications for the strategies, knowing which strategies students gravitate toward relative to the student life cycle is useful for instructors and student success practitioners more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".