Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review

Reference:

Avella, J., Kebritchi, M., Nunn, S., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning Journal, 20(2), 1-17. Retrieved from http://olj.onlinelearningconsortium.org/index.php/olj/article/view/790

Abstract: 

Higher education for the 21st century continues to promote discoveries in the field through learning analytics (LA). The problem is that the rapid embrace of of LA diverts educators’ attention from clearly identifying requirements and implications of using LA in higher education. LA is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of LA in higher education. Few studies have synthesized previous studies to provide an overview of LA issues in higher education. To address the problem, a systemic literature review was conducted to provide an overview of methods, benefits, and challenges of using LA in higher education. The literature review revealed that LA uses various methods including visual data analysis techniques, social network analysis, semantic, and educational data mining including prediction, clustering, relationship mining, discovery with models, and separation of data for human judgment to analyze data. The benefits include targeted course offerings, curriculum development, student learning outcomes, behavior and process, personalized learning, improved instructor performance, post-educational employment opportunities, and enhanced research in the field of education. Challenges include issues related to data tracking, collection, evaluation, analysis; lack of connection to learning sciences; optimizing learning environments, and ethical and privacy issues. Such a comprehensive overview provides an integrative report for faculty, course developers, and administrators about methods, benefits, and challenges of LA so that they may apply LA more effectively to improve teaching and learning in higher education.

This publication has been peer reviewed.
Publication Type: 
Journal Article
Authors: 
John T. Avella
Mansureh Kebritchi
Sandra G. Nunn
Therese Kanai
Year of Publication: 
2016
Journal, Book, Magazine or Other Publication Title: 
Online Learning Journal
Volume: 
20
Issue: 
2
Edition: 
Special Issue: Learning Analytics
Pages: 
17
Date Published: 
Wednesday, June 1, 2016
Publication Language: 
English
Editors: 
Karen Vignare
Boyer's Domain: 
Associated Awards: 
Excellence in Publishing Award
Honorarium

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