Abstract

In this paper, we present a system for visualizing learning logs of a course in progress together with predictions of learning activities of the following week and the final grades of students by state transition graphs. Data are collected from 236 students attending the course in progress and from 209 students attending the past course for prediction. From these data, the system constructs a state transition graph, where the prediction is based on the Markov property. We verify the performance of predictions by experiments in which the accuracy of prediction using the data of the course in progress and the one by 5-fold cross validation. [The research results have been achieved by "Research and Development on Fundamental and Utilization Technologies for Social Big Data" (178A03), the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan. For the complete proceedings, see ED579395.]

Citation

Okubo, F., Shimada, A. & Taniguchi, Y. (2017). A Visualization System for Predicting Learning Activities Using State Transition Graphs. Presented at International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) 2017. Retrieved June 8, 2023 from https://www.learntechlib.org/p/189703/.

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Keywords

• Accuracy
• College Students
• Computer Uses in Education
• Foreign Countries
• Grades (Scholastic)
• Graphs
• Integrated Learning Systems
• Learning Activities
• Markov Processes
• Prediction
• Questionnaires
• Validity