The Effect of Techniques of Using for Diagnose Students’ Mathematical Proficiency Level Through Machine Learning
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Abstract
The objectives of this research were: (1) to compare the results of using a technique to diagnose students' mathematical proficiency through machine learning between students who received feedback and have not received feedback and (2) to develop a predictive model for diagnosing the level of Mathematics proficiency of students through machine learning. The results revealed that comparing the mathematical proficiency of students in the mathematical process dimension and the conceptual structure dimension between the control and experimental groups were significantly different at .01. Moreover, the predictive model test results of data classification techniques by using the WEKA program with a decision tree method found that at the level of the students' proficiency in the mathematical process dimension, the best algorithms and techniques are Random Tree and Random Forest algorithms by Percentage Split technique 70%. Besides, at the level of the students' proficiency in conceptual structure dimension, the best algorithm and technique is the J48 algorithm by percentage split technique 70%.
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หากผู้เสนอบทความมีความจำเป็นเร่งด่วนในการตีพิมพ์โปรดส่งลงตีพิมพ์ในวารสารฉบับอื่นแทน โดยกองบรรณาธิการจะไม่รับบทความหากผู้เสนอบทความไม่ปฏิบัติตามเงื่อนไขและขั้นตอนที่กำหนดอย่างเคร่งครัด ข้อมูลของเนื้อหาในบทความถือเป็นลิขสิทธิ์ของ Journal of Inclusive and Innovative Education คณะศึกษาศาสตร์ มหาวิทยาลัยเชียงใหม่
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