The Impact of Study and Learning Strategies On Post-Secondary Student Academic Achievement: A Mixed-Methods Systematic Review
Bibliographic record
Abstract
BACKGROUNDWithin academic development, it is important for students to use effective study strategies to facilitate learning. Techniques used for long-term information retention include note taking strategies, time management, methods of self-testing and active recall. These strategies are explored to help students learn more effectively to attain their academic goals.METHODA mixed-methods systematic review of peer-review articles and grey literature was conducted with a predetermined criteria for a convergent integrated synthesis approach. PsychInfo (Ovid), Web of Science, and ProQuest databases were searched with guidance of a PICO-P logic grid and search strategy using keywords of student, study strategies, and achievement alongside filters. Initial studies were screened and reconciled by two independent authors with the use of a piloted screening tool. Using the Mixed Methods Assessment Tool (MMAT), included studies were assessed for quality. Two authors independently performed data extraction. Heterogeneity in study designs, outcomes, and measurements precluded meta and statistical analyses; thus, a qualitative analysis of studies was provided.RESULTSFour major themes contributing to academic performance were identified among the appraised articles. These themes were self-testing, scheduling/time management, concept maps, and learning styles. Self-testing, scheduling, and concept maps were positively correlated with increased academic performance, while no correlation was found with learning styles and academic performance.CONCLUSIONIncluded studies provided evidence for significant differences in study strategies implemented by high and low achieving students, such as areas of motivation for learning, efficiency, active recall, retrieval practices, and concept maps. Understanding the effectiveness of certain study strategies is critical for students and educational facilitators to maximize learning.
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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 | 0.036 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".