The Effect of Motivation for Learning Among High School Students and Undergraduate Students—A Comparative Study
Bibliographic record
Abstract
The current study was designed to identity if and to what extent differences in motivation exist between high school students, for whom school is mandatory, and undergraduate students in tertiary institutions, who make an active choice to study in an academic institution. This study also explores whether and to what extent motivation affects the achievements of these two groups of learners, and whether motivation is related to their personal, family, and socio-economic background and gender. To examine these questions, 121 participants responded to a 22-item questionnaire on motivation for learning. Findings show that undergraduate students are more highly motivated for learning compared to high school students. Associations were found between learners’ personal and academic background and their motivation: Motivation increases with age and as grade average increases. A significant difference was, however, found in motivation levels between learners with average socio-economic status and learners with above-average socio-economic status. No gender effects in learners’ motivation were found. Findings of the study shed light on the significant of motivation in high school, which is a significant period in youngsters’ lives. High school is a scholastic space that also has the potential to strengthen motivation for learning in the future, in academic studies, as both education systems – high school and academic education – affect each other.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".