Developing Numbers and Algebra Outcomes Using an Intelligent Tutoring System
Bibliographic record
Abstract
Mathematical proficiency, particularly in Numbers and Algebra outcomes, is critical for academic achievement and real-world problem-solving. This study examines the impact of an intelligent tutoring system on seventh-grade students’ mathematical development. The research had two goals: (1) to compare the conceptual understanding between experimental and control groups, and (2) to assess learning progression over time. Eighty-four seventh-grade students from the Demonstration School of Khon Kaen University (Modindaeng), Thailand, were selected through systematic random sampling. Students were assigned either to an experimental group (42 students) that used intelligent tutoring with automated feedback or to a control group (42 students) using the same system without feedback. Research tools included a mathematical ability assessment and intelligent tutoring lessons. Data were analyzed through an independent samples t-test, repeated measures ANOVA, and relative gain scores. The results showed that: (1) the experimental group achieved significantly greater improvement (p < .05), with mean scores rising from 0.33 (SD = 0.78) to 1.67 (SD = 1.57), compared to 0.33 (SD = 0.84) to 0.95 (SD = 1.30) in the control group; and (2) learning progression was significantly higher in the experimental group (p = .003), with a 40.67% (SD = 38.96) average gain versus 19.64% (SD = 30.00) for the control group. The findings confirm that diagnostic-based intelligent tutoring significantly boosts students’ conceptual understanding and learning growth, underlining the potential of digital technology to drive future innovations in mathematics education.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".