Developing Mathematical Procedures in Numbers and Algebra Through an Intelligent Tutoring System
Bibliographic record
Abstract
This study aimed to assess and compare the development of mathematical procedures on the part of Grade 7 students using an intelligent tutoring system on a digital platform. The sample comprised 96 students from Khon Kaen University Demonstration School, Thailand, divided equally into experimental and control groups. The experimental group worked with an intelligent tutoring system and received automated feedback, while the control group worked with an intelligent tutoring system but did not receive such feedback. Data were analyzed using mean, standard deviation, t-test, repeated measures ANOVA, and learning progression analysis. The results showed that the experimental group significantly improved in terms of mathematical procedures (p < .05), with pre-test and post-test scores of 1.31 (SD = 1.49) and 3.46 (SD = 0.97), respectively. In contrast, the control group’s scores were 1.13 (SD = 1.41) and 2.67 (SD = 1.56). Additionally, the experimental group achieved an average learning progression of 84.83% (SD = 27.59), significantly higher than that of the control group’s 64.54% (SD = 30.08) (t = 3.45, df = 94, p < .001), with a large effect size ( = 0.70). In conclusion, integrating diagnostic systems with intelligent tutoring and automated feedback effectively enhances students’ mathematical proficiency and learning progression. These findings support the use of digital technology to improve mathematics education.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".