Re-Designing The Structure Of Online Courses To Empower Educational Data Mining

Keywords

Clustering analysis; Data interpretability; Online instructional design; Supporting teachers

Abstract

The amount of information contained in any educational data set is fundamentally constrained by the instructional conditions under which the data are collected. In this study, we show that by redesigning the structure of traditional online courses, we can improve the ability of educational data mining to provide useful information for instructors. This new design, referred to as Online Learning Modules, blends frequent learning assessment as seen in intelligent tutoring systems into the structure of conventional online courses, allowing learning behavior data and learning outcome data to be collected from the same learning module. By applying relatively straightforward clustering analysis to data collected from a sequence of four modules, we are able to gain insight on whether students are spending enough time studying and on the effectiveness of the instructional materials, two questions most instructors ask each day.

Publication Date

1-1-2018

Publication Title

Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85084016934 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85084016934

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