Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis
Author(s) -
Konomu Dobashi
Publication year - 2017
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2017.08.222
Subject(s) - computer science , table (database) , course (navigation) , viewpoints , preprocessor , data pre processing , section (typography) , information retrieval , class (philosophy) , multimedia , world wide web , data mining , artificial intelligence , operating system , art , physics , astronomy , visual arts
This paper describes a data integration method for Moodle course logs and pivot table functions to analyze the behavior of students material page views in face-to-face blended learning using Moodle course materials. The developed method integrates the data with a pivot table by preprocessing Moodle course logs and generates a time series cross section (TSCS) table that visualizes the students course material page views. Experiments conducted on Moodle page views of actual materials collected during actual lessons found that the table visualizes both overall and individual viewpoints. Reactions to teacher instructions on course materials during class can also be visualized by the generated TSCS table. Moreover, because students who open course items late or do not open them can be identified clearly, the method can be used as a reference for improving future classes.
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