Faculty of Computer Science and Information Systems, University of Malaya

Abstract

In the context of one-on-one instruction, reflective dialogues help students advance their learning and improve their problem solving ability. The effectiveness of one-on-one instruction with respect to learning through dialogue is highlighted by researchers and educators. However, little if any, is known about how reflective dialogues may lead to learning improvement and predict students’ problem solving ability. This information can be extracted from large educational datasets using data mining techniques. Consequently, this study aims at mining USNA physics data set applying a two-level clustering approach to find patterns in the data and identify how reflective dialogues predict students’ problem solving ability. The results indicated that reflective group performed better on the hourly exams. Control subjects took lower average count of steps during problem solving activity and the average of duration was longer in control group. Also, higher average of correct answers and average count of attempts was found in reflective condition. Yet, control group had a higher level of incorrect answers as compared to reflective group. During the intervention, reflective subjects had higher level of average count of attempts and lesser average count of deletion. Reflective group asked for less hints, had lesser count of problem and requested for calculator less than control subjects. The results of the analysis help educators plan more effective tutorial dialogues in Intelligent Tutoring Systems (ITSs).