Using PLS-SEM to Examine the Structure of First-year University Students’ Mathematics-related Beliefs
Bibliographic record
Abstract
Many research indicated that more and more students choose to drop out of mathematics-related subjects during university study, especially in the western context. Besides the difficulty of mathematics content, first-year university students also face issues of the transition period. Identifying the impact of first-year university students' belief factors on their persistence in mathematics study needed further research. This study served as a pilot study; it structured the framework of first-year university students’ mathematics-related beliefs in relation to students’ persistence on the further mathematics study. A two-stage approach of using PLS-SEM to assessing the conceptual framework was introduced in detail. The relationships of dimensions of students’ epistemological beliefs about mathematics, self-efficacy, self-regulated learning strategies and perceptions about learning environment were assessed. This study provides the feasibility for future follow-up studies to examine mathematics-related beliefs and intentions to continue learning among university students on a larger scale.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".