Proposal Abstract

Recently, there have been governmental demands to increase student success in higher education (e.g., Obama, B. 2009). One way to increase student success is to increase retention in courses. Among the strategies for doing so, �learning analytics� offers much promise. Briefly, learning analytics is � the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the and the environments in which it occurs� (Long & Siemens, 2011, p. 32). The objective of this presentation is to show how faculty can make use of statistics available in their Learning Management System (LMS) to make predictions about student performance. This presentation will provide data mined from an LMS to predict student performance on various aspects of the course, as well as student retention. Attendees can expect to learn how to use data in their own LMS to monitor and predict student performance.

Location

Concourse

Publication Type and Release Option

Presentation (Open Access)

 
Mar 28th, 4:00 PM Mar 28th, 5:30 PM

Using Data from a Learning Management System to Monitor Student Performance

Concourse

Recently, there have been governmental demands to increase student success in higher education (e.g., Obama, B. 2009). One way to increase student success is to increase retention in courses. Among the strategies for doing so, �learning analytics� offers much promise. Briefly, learning analytics is � the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the and the environments in which it occurs� (Long & Siemens, 2011, p. 32). The objective of this presentation is to show how faculty can make use of statistics available in their Learning Management System (LMS) to make predictions about student performance. This presentation will provide data mined from an LMS to predict student performance on various aspects of the course, as well as student retention. Attendees can expect to learn how to use data in their own LMS to monitor and predict student performance.