Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics

The use of analytics in education provides researchers the opportunity to uncover student engagement habits by utilizing data generated through online platforms such as course learning management systems (LMS). Student engagement has been shown to vary based on student-instructor interaction. We exa...

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Main Authors: Cory Brozina, David B. Knight, Timothy Kinoshita, Aditya Johri
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8861038/
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spelling doaj-984822d79ab74612b04ee2bd3c45a1eb2021-03-30T00:53:47ZengIEEEIEEE Access2169-35362019-01-01716368616369910.1109/ACCESS.2019.29458738861038Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through AnalyticsCory Brozina0https://orcid.org/0000-0001-7461-8282David B. Knight1Timothy Kinoshita2Aditya Johri3Department of Mechanical, Industrial, and Manufacturing Engineering, Youngstown State University, Youngstown, OH, USADepartment of Engineering Education, Virginia Tech, Blacksburg, VA, USADepartment of Engineering Education, Virginia Tech, Blacksburg, VA, USADepartment of Information Sciences and Technology, George Mason University, Fairfax, VA, USAThe use of analytics in education provides researchers the opportunity to uncover student engagement habits by utilizing data generated through online platforms such as course learning management systems (LMS). Student engagement has been shown to vary based on student-instructor interaction. We examined LMS usage of first-year engineering students in a large research university in the United States to examine the following three research questions: 1) How do course grades vary based on the students' instructor and the overall number of LMS sessions per student, 2) How do course grades vary based on the students' instructor and the number of LMS sessions per student for different course tools, and 3) How does the timing and frequency of LMS tool usage relate to course grades and vary across instructors? We found a positive relationship between LMS usage and course grades; however, the relationship is dependent upon the instructor of the course, as well as for the specific type of tool used. We also found that the day of the week on which the LMS was used is a strong predictor of student course grades. The results empirically demonstrate that better engagement with a course leads to better outcomes and there are variations in how instructors use an LMS which ultimately influences student usage and performance. We also illustrate an opportunity for researchers and instructors to capture, analyze, and use LMS data to inform and improve teaching practices and policies.https://ieeexplore.ieee.org/document/8861038/Assessmentengagementengineering educationfirst-year engineeringlearning management systems
collection DOAJ
language English
format Article
sources DOAJ
author Cory Brozina
David B. Knight
Timothy Kinoshita
Aditya Johri
spellingShingle Cory Brozina
David B. Knight
Timothy Kinoshita
Aditya Johri
Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
IEEE Access
Assessment
engagement
engineering education
first-year engineering
learning management systems
author_facet Cory Brozina
David B. Knight
Timothy Kinoshita
Aditya Johri
author_sort Cory Brozina
title Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
title_short Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
title_full Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
title_fullStr Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
title_full_unstemmed Engaged to Succeed: Understanding First-Year Engineering Students’ Course Engagement and Performance Through Analytics
title_sort engaged to succeed: understanding first-year engineering students’ course engagement and performance through analytics
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description The use of analytics in education provides researchers the opportunity to uncover student engagement habits by utilizing data generated through online platforms such as course learning management systems (LMS). Student engagement has been shown to vary based on student-instructor interaction. We examined LMS usage of first-year engineering students in a large research university in the United States to examine the following three research questions: 1) How do course grades vary based on the students' instructor and the overall number of LMS sessions per student, 2) How do course grades vary based on the students' instructor and the number of LMS sessions per student for different course tools, and 3) How does the timing and frequency of LMS tool usage relate to course grades and vary across instructors? We found a positive relationship between LMS usage and course grades; however, the relationship is dependent upon the instructor of the course, as well as for the specific type of tool used. We also found that the day of the week on which the LMS was used is a strong predictor of student course grades. The results empirically demonstrate that better engagement with a course leads to better outcomes and there are variations in how instructors use an LMS which ultimately influences student usage and performance. We also illustrate an opportunity for researchers and instructors to capture, analyze, and use LMS data to inform and improve teaching practices and policies.
topic Assessment
engagement
engineering education
first-year engineering
learning management systems
url https://ieeexplore.ieee.org/document/8861038/
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AT timothykinoshita engagedtosucceedunderstandingfirstyearengineeringstudentsx2019courseengagementandperformancethroughanalytics
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