Using learning analytics to predict (and improve) student success: a faculty perspective

Abstract Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics in their own courses to help their students succeed. In this paper, we define learning analytics, how it has been used in educational institutions, what learning analytics tools are available, and how faculty can make use of data in their courses to monitor and predict student performance. Finally, we discuss several issues and concerns with the use of learning analytics in higher education.

[1]  Laurie P. Dringus,et al.  Learning Analytics Considered Harmful. , 2012 .

[2]  Vernon C. Smith,et al.  Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses , 2012 .

[3]  Wu He,et al.  Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade , 2012, J. Educ. Technol. Soc..

[4]  Charles D. Dziuban,et al.  Analytics that Inform the University: Using Data You Already Have , 2012 .

[5]  Linda Corrin,et al.  Learning Analytics: A Case Study of the Process of Design of Visualizations , 2012 .

[6]  George Siemens,et al.  Penetrating the fog: analytics in learning and education , 2014 .

[7]  Stephanie J. Jones Technology Review: The Possibilities of Learning Analytics to Improve Learner-Centered Decision-Making , 2012 .

[8]  Dennis Zielke,et al.  Design and Implementation of a Learning Analytics Toolkit for Teachers , 2012, J. Educ. Technol. Soc..

[9]  Erik Duval,et al.  Dataset-Driven Research to Support Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[10]  Hendrik Drachsler,et al.  Translating Learning into Numbers: A Generic Framework for Learning Analytics , 2012, J. Educ. Technol. Soc..

[11]  Hae Okimoto,et al.  The PAR Framework Proof of Concept: Initial Findings from a Multi-Institutional Analysis of Federated Postsecondary Data , 2012 .

[12]  Zane L. Berge,et al.  Learning analytics as a tool for closing the assessment loop in higher education , 2012 .

[13]  Freeman A. Hrabowski,et al.  Assessment and Analytics in Institutional Transformation , 2011 .

[14]  Rebecca Ferguson,et al.  Social Learning Analytics , 2012, J. Educ. Technol. Soc..

[15]  Shane Dawson,et al.  Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan , 2012, J. Educ. Technol. Soc..

[16]  Anthony G. Picciano The Evolution of Big Data and Learning Analytics in American Higher Education , 2012 .

[17]  Larry Johnson,et al.  The NMC Horizon Report: 2012 Higher Education Edition. , 2012 .

[18]  Philip J. Goldstein,et al.  Academic Analytics : The Uses of Management Information and Technology in Higher Education , 2005 .

[19]  W. F. Punch,et al.  Predicting student performance: an application of data mining methods with an educational Web-based system , 2003, 33rd Annual Frontiers in Education, 2003. FIE 2003..

[20]  Austin Gibbons,et al.  Learning Analytics , 2014, Encyclopedia of Social Network Analysis and Mining.

[21]  John P. Campbell,et al.  Academic Analytics: A New Tool for a New Era. , 2007 .

[22]  Jafar Habibi,et al.  Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning , 2010, EDM.