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

Javeed Kittur; Jennifer M. Bekki; Samantha Ruth Brunhaver

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Conference: 2020 ASEE Virtual Annual Conference Content Access
Location: Virtual On line
Publication Date: June 22, 2020
Start Date: June 22, 2020
End Date: June 26, 2021
Conference Session: Teaching and Learning in Online Environments
Tagged Division: Educational Research and Methods
Page Count: 18
DOI: 10.18260/1-2--34896
Permanent URL: https://peer.asee.org/34896
Download Count: 74

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Paper Authors

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Javeed Kittur Arizona State University orcid.org/0000-0001-6132-7304

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Javeed Kittur is a doctoral student (Engineering Education Systems & Design) at Arizona State University, USA. He received a Bachelor’s degree in Electrical and Electronics Engineering and Master's in Power System from India in 2011 and 2014 respectively. He has worked with Tata Consultancy Services as Assistant Systems Engineer from 2011-2012, Bangalore, India. He has worked as an Assistant Professor (2014 to 2018) in the department of Electrical and Electronics Engineering, KLE Technological University, India. He is a certified IUCEE International Engineering Educator. He is awarded with the ‘Ing.Paed.IGIP’ title at ICTIEE, 2018.

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Jennifer M. Bekki Arizona State University

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She teaches courses in the engineering and manufacturing engineering programs as well as programs in the Engineering Education Systems and Design PhD program. Her research interests include topics related to student persistence, STEM doctoral student experiences, faculty mentorship and development, modeling and analysis of complex manufacturing systems, and the development of new discrete event simulation methodologies. Bekki is the co-director of the interdisciplinary, National Science Foundation supported CareerWISE research program, which strives to: 1) understand the experiences of diverse women who are pursuing and leaving doctoral programs in science and engineering and 2) increase women’s persistence in science and engineering doctoral programs through the development and dissemination of an online resilience and interpersonal communication training program.

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Samantha Ruth Brunhaver Arizona State University

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Samantha Brunhaver is an Assistant Professor of Engineering in the Fulton Schools of Engineering Polytechnic School. Dr. Brunhaver recently joined Arizona State after completing her M.S. and Ph.D. in Mechanical Engineering at Stanford University. She also has a B.S. in Mechanical Engineering from Northeastern University. Dr. Brunhaver's research examines the career decision-making and professional identity formation of engineering students, alumni, and practicing engineers. She also conducts studies of new engineering pedagogy that help to improve student engagement and understanding.

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Abstract

This work falls under the evidence-based practice type of paper. Online undergraduate engineering education is rapidly increasing in use. The online format not only provides greater flexibility and ease of access for students, but also has lower costs for universities when compared to face-to-face courses. Even with these generally positive attributes, online courses face challenges with respect to student attrition. Numerous studies have shown that the dropout rate in online courses is higher than that for in-person courses, and topics related to online student persistence remain of interest.

Data describing student interactions with their Learning Management System (LMS) provide an important lens through which online student engagement and corresponding persistence decisions can be studied, but many engineering education researchers may lack experience in working with LMS interaction data. The purpose of this paper is to provide a concrete example for other engineering education researchers of how LMS interaction data from online undergraduate engineering courses can be prepared for analysis. The work presented here is part of a larger National Science Foundation-funded study dedicated to developing a theoretical model for online undergraduate engineering student persistence based on student LMS interaction activities and patterns.

Our sample dataset includes six courses, two from electrical engineering and four from engineering management, offered during the fall 2018 semester at a large, public southwestern university. The 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. Relatedly, the features created from the data in this study can be classified into three categories: 1) learning page views, which capture student interactions with course content, 2) procedural page views, which capture student navigation to course management activities, and 3) social page views, which capture learner-to-learner and learner-to-instructor interactions.

The full paper will provide the rationale and details involved in choices related to data cleaning, manipulation, and feature creation. A complete list of features will also be included. These features will ultimately be combined with associative classification to discover relationships between student-LMS interactions and persistence decisions.

Citation
Format

Kittur, J., & Bekki, J. M., & Brunhaver, S. R. (2020, June), 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 Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34896

TY - CPAPER
AB - This work falls under the evidence-based practice type of paper. Online undergraduate engineering education is rapidly increasing in use. The online format not only provides greater flexibility and ease of access for students, but also has lower costs for universities when compared to face-to-face courses. Even with these generally positive attributes, online courses face challenges with respect to student attrition. Numerous studies have shown that the dropout rate in online courses is higher than that for in-person courses, and topics related to online student persistence remain of interest.

Data describing student interactions with their Learning Management System (LMS) provide an important lens through which online student engagement and corresponding persistence decisions can be studied, but many engineering education researchers may lack experience in working with LMS interaction data. The purpose of this paper is to provide a concrete example for other engineering education researchers of how LMS interaction data from online undergraduate engineering courses can be prepared for analysis. The work presented here is part of a larger National Science Foundation-funded study dedicated to developing a theoretical model for online undergraduate engineering student persistence based on student LMS interaction activities and patterns.

Our sample dataset includes six courses, two from electrical engineering and four from engineering management, offered during the fall 2018 semester at a large, public southwestern university. The 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. Relatedly, the features created from the data in this study can be classified into three categories: 1) learning page views, which capture student interactions with course content, 2) procedural page views, which capture student navigation to course management activities, and 3) social page views, which capture learner-to-learner and learner-to-instructor interactions.

The full paper will provide the rationale and details involved in choices related to data cleaning, manipulation, and feature creation. A complete list of features will also be included. These features will ultimately be combined with associative classification to discover relationships between student-LMS interactions and persistence decisions.

AU - Javeed Kittur
AU - Jennifer M. Bekki
AU - Samantha Ruth Brunhaver
CY - Virtual On line
DA - 2020/06/22
PB - ASEE Conferences
TI - 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
UR - https://peer.asee.org/34896
DO - 10.18260/1-2--34896
ER -