The Relationship between Teaching Skills, Academic Emotion, Academic Stress and Mindset in University Student Academic Achievement Prediction: A PLS-SEM Approach
Why this work is in the frame
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Bibliographic record
Abstract
This cross-sectional study conducted to develop a model for predicting academic achievement of university students by investigating the relationship between teaching skills, academic emotions (positive and negative), and academic stress associated with Mindset (growth and fixed) using structural equation modeling. The statistical population consisted of 360 students of the Islamic Azad University of Hamedan who were selected randomly using a relative stratified method. The study was descriptive and correlational. The data were analyzed by SPSS version 25 and SmartPLS version 3.2.8. First, the validity of the model was estimated using Cronbach's alpha, composite reliability, convergent validity, and divergent validity; then, the coefficient of determination, effect size, and Stone-Geisser criterion were calculated for evaluating the structural model. The results showed that the validity and adequacy of the suggested model were suitable. Thus, it could be used in different situations by experts in related areas. The relationship between growth Mindset and academic achievement was significant; growth Mindset moderated the effect of negative emotion and stress on academic achievement the crucial role of professor skills in the academic achievement of students was confirmed directly or through its effect on positive emotion. The effect of teaching skills was not significant on the academic achievement of students with fixed Mindset, while the effect of academic stress confirmed on these students. Therefore, the identification of students with fixed Mindset and psychological interventions for these students can be useful in their academic achievement and their mental health.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| 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 it