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Developing Mathematical Procedures in Numbers and Algebra Through an Intelligent Tutoring System
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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.
Canadian Center of Science and Education
Title: Developing Mathematical Procedures in Numbers and Algebra Through an Intelligent Tutoring System
Description:
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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