A visualization system for predicting learning activities using state transition graphs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017
EditorsJ. Michael Spector, Dirk Ifenthaler, Dirk Ifenthaler, Demetrios G. Sampson, Pedro Isaias, Luis Rodrigues
PublisherIADIS Press
Pages173-180
Number of pages8
ISBN (Electronic)9789898533685
Publication statusPublished - Jan 1 2017
Event14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017 - Vilamoura, Algarve, Portugal
Duration: Oct 18 2017Oct 20 2017

Publication series

Name14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017

Conference

Conference14th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2017
CountryPortugal
CityVilamoura, Algarve
Period10/18/1710/20/17

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Education
  • Computer Science Applications

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