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Learning Management Systems: What More can we Know?

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

Data Analysis and Assessment

Tagged Division

Computers in Education

Page Count


Page Numbers

26.1072.1 - 26.1072.14



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Paper Authors


Cory Brozina Virginia Tech

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Cory Brozina is a PhD Candidate in Engineering Education at Virginia Tech. He has his B.S. and M.S. in Industrial & Systems Engineering also from Virginia Tech. His research interests are in Learning Analytics, Engineering Education Assessment, and Educational Technology.

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David B. Knight Virginia Tech, Department of Engineering Education Orcid 16x16

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David Knight is an Assistant Professor in the Department of Engineering Education and affiliate faculty with the Higher Education Program, Center for Human-Computer Interaction, and Human-Centered Design Program. His research focuses on student learning outcomes in undergraduate engineering, learning analytics approaches to improve educational practices and policies, interdisciplinary teaching and learning, organizational change in colleges and universities, and international issues in higher education.

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Learning Management Systems: What can it tell us about student engagement?Learning management systems (LMS) are ubiquitous among colleges and universitiesworldwide, however they are thought of as a transactional warehouse rather than an opportunityto understand student learning outside of the classroom. Each LMS is used differently acrosscampuses, and even across sections of the same courses taught by different instructors. Forinstance, one instructor might utilize a gradebook feature which allows students to view theirassignment grades, while another instructor in the same course might use a different method todistribute grades for assignments. But is there a differential relationship between tool usage andstudent engagement or performance in the course across these sections? Our research addressesthis issue and seeks to understand the nature of how LMS tools are used by students and how theuse of those tools may shed insight on student learning or engagement.We ground our work theoretically using the Academic Plan Model to understand how freshmanengineering students’ use of LMS tools relate to their performance in the class. The AcademicPlan Model details potential influences on curriculum design at the course, program, andinstitutional levels. As the Model suggests, faculty members may (or should) consider learners,instructional resources, and instructional processes when developing their curricular plans. Priorresearch within and outside engineering, however, has shown that faculty tend not to draw onavailable data when considering these components, if they even consider them at all. Our studypresents an idea for bringing data into those considerations by focusing on the course-levelactivities of students within an LMS. We empirically describe an opportunity educators have tounderstand what can be learned from investigating LMS student data. Specifically, our data setconsists of student LMS log files (approximately 12 million rows) for three engineering courses(54 sections and over 1,300 students) from Fall 2013 through Spring 2014.Results show clear patterns of student engagement with different LMS tools across final gradeswithin first year engineering courses. Additionally, certain tool usage relates more strongly withcourse performance. By understanding how and when students use those tools in particular,faculty members may be able to create more data-informed course plans and provide empiricallydriven feedback to students on their levels of engagement in the class.

Brozina, C., & Knight, D. B. (2015, June), Learning Management Systems: What More can we Know? Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24409

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