NDLI: Applying classification techniques on temporal trace data for shaping student behavior models

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Applying classification techniques on temporal trace data for shaping student behavior models

Content Provider	ACM Digital Library
Author	Economides, Anastasios A. Karapistoli, Eirini Papamitsiou, Zacharoula
Abstract	Differences in learners' behavior have a deep impact on their educational performance. Consequently, there is a need to detect and identify these differences and build suitable learner models accordingly. In this paper, we report on the results from an alternative approach for dynamic student behavioral modeling based on the analysis of time-based student-generated trace data. The goal was to unobtrusively classify students according to their time-spent behavior. We applied 5 different supervised learning classification algorithms on these data, using as target values (class labels) the students' performance score classes during a Computer-Based Assessment (CBA) process, and compared the obtained results. The proposed approach has been explored in a study with 259 undergraduate university participant students. The analysis of the findings revealed that a) the low misclassification rates are indicative of the accuracy of the applied method and b) the ensemble learning (treeBagger) method provides better classification results compared to the others. These preliminary results are encouraging, indicating that a time-spent driven description of the students' behavior could have an added value towards dynamically reshaping the respective models.
Starting Page	299
Ending Page	303
Page Count	5
File Format	PDF
ISBN	9781450341905
DOI	10.1145/2883851.2883926
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learner behavioral modeling Supervised learning classification Assessment analytics Computer-based testing
Content Type	Text
Resource Type	Article

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