Investigation of the Predictive Power of Academic Achievement, Learning Approaches and Self-Regulatory Learning Skills on University Entrance Exam Scores Using Path Analysis
Bibliographic record
Abstract
A good analysis of the success factors in the university entrance exam, which is an important step for academiccareers of students, is believed to help them manage this process. Properties such as self-regulation and learningapproaches adopted by students undoubtedly influence their academic achievement as well as their success inuniversity entrance exams. However, it is not exactly known how the direct and indirect relations between thesevariables are, and which variable has more effect on success. This research aims to determine the extent to whichuniversity entrance exam score as dependant variable; and academic achievement, deep, surface and strategiclearning approaches, four sub-dimensions of self-regulatory learning skills scale as independent variables to predictuniversity entrance exam score directly and indirectly; to this end, a path model was developed. Within the scope ofthe research, the data obtained from 445 students in the 4th class of the state-affiliated high schools in the 2016-2017academic year were used. As a result of the research, the most important factor affecting the success of universityentrance exam was found to be diploma grade; while diploma grades raise by using deep learning approaches, theyfall by using surface learning approaches. It was detected that the use of the strategic learning approach reducesuniversity entrance exam scores.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".