Learner analytics in engineering education: A detailed account of practices used in the cleaning and manipulation of learning management system data from online undergraduate engineering courses

Research output: Contribution to journalConference articlepeer-review

Abstract

This is a research paper that provides a concrete example for other engineering education researchers of how Learning Management System (LMS) interaction data from online undergraduate engineering courses can be prepared for analysis. We provide the rationale and details involved in choices related to data preparation, feature creation, and feature selection as part of a larger National Science Foundation-funded study dedicated to developing a theoretical model for online undergraduate engineering student persistence. LMS interaction data provides details about students' navigations to and submissions of different course elements including quizzes, assignments, discussion forums, wiki pages, attachments, modules, the syllabus, the gradebook, and course announcements. The sample dataset presented here includes 32 courses from three ABET accredited fully online engineering degree programs, electrical engineering, engineering management, and software engineering, offered at a large, public, southwestern university. The analysis demonstrated in this paper will ultimately be combined with associative classification to discover relationships between student-LMS interactions and persistence decisions.

Original languageEnglish (US)
Article number940
JournalASEE Annual Conference and Exposition, Conference Proceedings
Volume2020-June
StatePublished - Jun 22 2020
Event2020 ASEE Virtual Annual Conference, ASEE 2020 - Virtual, Online
Duration: Jun 22 2020Jun 26 2020

ASJC Scopus subject areas

  • Engineering(all)

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