Enhancing Students’ Learning Outcomes in Mathematics through Intelligent Tutoring Systems Based on Real-Time Feedback
Bibliographic record
Abstract
This study examined the effectiveness of an intelligent tutoring system (ITS) driven by real-time feedback in enhancing students’ mathematical learning outcomes, as defined by the Structure of Observed Learning Outcomes. Conducted within the domains of Measurement and Geometry, the study employed a randomized controlled trial involving 120 students from the Mathematics Program for Gifted Students at Khon Kaen University Demonstration School. Of these, 78 were selected through systematic random sampling and assigned to experimental and control groups. The experimental group engaged with the ITS featuring automated, real-time feedback, while the control group used the same system without feedback. Both groups utilized the system through adaptive diagnostic assessments. The diagnostic test demonstrated strong psychometric properties (Rasch model difficulty range = -2.34 to 1.71; Cronbach’s α = 0.81; IRT reliability = 0.89). Statistical analyses—including independent t-tests, repeated measures ANOVA, and relative gain scores—revealed significant learning gains in the experimental group (p < .05). Mean scores increased from 1.90 to 3.54 in the experimental group and from 1.90 to 2.85 in the control group. The experimental group reached an advanced level (M = 84.83%), significantly outperforming the control group (M = 61.54%), with an effect size of 0.73. These findings highlight the potential of ITS with diagnostic feedback to foster deep, structured mathematical understanding and offer valuable insights for personalized digital learning.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".