T1-F: A Novel Study of the Relation Between Students’ Navigational Behavior on Blackboard and their Learning Performance in an Undergraduate Networking Course

Location

Coastal

Start Date

5-3-2018 10:00 AM

Description

This paper provides an overview of students’ behavior analysis on a learning management system (LMS), Blackboard (Bb) Learn for a core data communications course of the Undergraduate IT program in the Information Sciences and Technology (IST) Department at George Mason University (GMU). This study is an attempt to understand the navigational behavior of students on Blackboard Learn which can be further attributed to the overall performance of the students. In total, 160 undergraduate students participated in the study. Vast amount of student’s activities data across all four sections of the course were collected. All sections have similar content, assessment design and instruction methods. A correlation analysis between the different assessment methods and various key variables such as total student time, total number of logins and various other factors were performed, to evaluate students’ engagement on Blackboard Learn. Our findings can help instructors to efficiently identify students’ strengths or weaknesses and fine-tune their courses for better student engagement and performance.

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Mar 5th, 10:00 AM

T1-F: A Novel Study of the Relation Between Students’ Navigational Behavior on Blackboard and their Learning Performance in an Undergraduate Networking Course

Coastal

This paper provides an overview of students’ behavior analysis on a learning management system (LMS), Blackboard (Bb) Learn for a core data communications course of the Undergraduate IT program in the Information Sciences and Technology (IST) Department at George Mason University (GMU). This study is an attempt to understand the navigational behavior of students on Blackboard Learn which can be further attributed to the overall performance of the students. In total, 160 undergraduate students participated in the study. Vast amount of student’s activities data across all four sections of the course were collected. All sections have similar content, assessment design and instruction methods. A correlation analysis between the different assessment methods and various key variables such as total student time, total number of logins and various other factors were performed, to evaluate students’ engagement on Blackboard Learn. Our findings can help instructors to efficiently identify students’ strengths or weaknesses and fine-tune their courses for better student engagement and performance.