The mediating role of students' engagement and satisfaction in the relationship between AI-based educational tools, flexible learning techniques, and the performance of students on academic probation
Bibliographic record
Abstract
This study investigates the influence of Artificial Intelligence-based (AI-based) educational tools and flexible learning techniques on the academic performance of students on academic probation, emphasizing the mediating roles of students’ satisfaction and engagement. Conducted at the Canadian International College (CIC) in Egypt, the study targeted 240 probationary students using a census approach. A mixed-methods design was employed, incorporating surveys, academic records, and interviews. Structural Equation Modeling (SEM) revealed that AI tools and flexible techniques significantly enhance student satisfaction and engagement, which in turn positively affect academic performance. The findings demonstrate partial mediation, with a substantial portion of the variance in academic performance explained by satisfaction and engagement. A comparative analysis across six academic terms also showed a notable improvement in success rates during the study year, reinforcing the model’s effectiveness.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".