Abstract
Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.
Highlights
Adaptive Instructional Systems (AISs) are recognised as a wider category of technology-enhanced learning systems that inherit all characteristics of the early Computer Assisted Instruction (CAI) systems and more recent Intelligent Tutoring Systems (ITSs)
In terms of the Learning Analytics (LA) variables that can be used to describe the tutoring process in the Tutomat, the variables related to gained knowledge are not relevant because all students who participated in the experiment finished the tutoring process at the highest level – the expert stereotype
The LA variables collected by the Tutomat and selected for further analysis include: 1) the number of tutoring cycles needed to finish the tutoring process – #Tutoring cycles; 2) the total time spent in the learning and teaching phase of the tutoring process – #Time learning; 3) the total time spent in the testing phase of the tutoring process – #Time testing; and 4) the total time spent in the evaluation phase of the tutoring process – #Time evaluation
Summary
Adaptive Instructional Systems (AISs) are recognised as a wider category of technology-enhanced learning systems that inherit all characteristics of the early Computer Assisted Instruction (CAI) systems and more recent Intelligent Tutoring Systems (ITSs). At the end of the tutoring process, various LA variables can be analysed, such as the number of domain knowledge components seen in the tutoring process, total time spent in different aspects of the tutoring process, total score gained online, the number of logins, etc. This valuable information about individual characteristics and group differences is consequentially analysed and used by AIS
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