TY - JOUR
T1 - Learner analytics in engineering education
T2 - 2020 ASEE Virtual Annual Conference, ASEE 2020
AU - Kittur, Javeed
AU - Bekki, Jennifer M.
AU - Brunhaver, Samantha Ruth
N1 - Funding Information:
This paper is based on research supported by the National Science Foundation (NSF) under Award Number 1825732. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
Publisher Copyright:
© American Society for Engineering Education 2020.
PY - 2020/6/22
Y1 - 2020/6/22
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:85095755528
SN - 2153-5965
VL - 2020-June
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
M1 - 940
Y2 - 22 June 2020 through 26 June 2020
ER -