Learner Analytics for Student Success

Concurrent Session 5

Brief Abstract

The goal of this session is to share a pilot focusing on student success in correlation to learner analytics in LMS.  The preliminary data revealed distinctive characteristics in relation to grades. This session also provides a venue for collaboration of others interested in using online analytics to increase student success.


Director of Educational Technology, University of Texas at San Antonio.

Extended Abstract

An increasing number of universities and colleges have implemented integrated planning and advising for student success. They have explored the rewards and challenges of student success planning systems that can track students' progress toward educational goals and trigger interventions based on student behavior data. Despite benefits and promise, it is desirable that the system in place allows for realtime student performance data on a granual-level because there is a real need for just-in-time interventions.

This session aims to share a current pilot in a business school at a large-size urban university that focuses on student success in correlation to their online access in course LMSs. LMSs can be analyzed to gather formative data on student performance and engagement.  By using analytics, the college can gauge students’ interaction with course materials and track to jumpstart earlier interventions for those needing it most.

So, how does it work?

Examples of data that can be accessed in course content may include the first date of access, content, discussion boards, course tools and online submissions.  The preliminary findings are based on one introductory accounting course with twelve sections in Fall 2018 (n = 834).  In order to visualize the results, I used heat maps to display the frequency of course access within all twelve sections.  

The  results indicated different access patterns, varying with the course grade. Characteristically, there were distinctive patterns displayed, depending on students’ grades in the class. As part of an ongoing process, additional data will be extracted to discover more about the characteristics of successful students. 


There will be a mixed mode of presentation. First, I will introduce the characteristics of my students.  At the same time, I will conduct online polling to include the audience in a discussion.  On the side, I will display the audience results, along with my own.  We will discuss what kind of outcomes they would expect if they did the same research on their campuses.  


My goal is to begin a discussion with the audience and create a venue for people with similar ideas to collaborate.  I will ask the participants if they are interested in participating in further research on this topic.