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

Sandra Nunn,Therese Kanai,John T Avella,Mansureh Kebritchi

doi:10.24059/olj.v20i2.790

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.

Highlights

The advancement of technology has provided the opportunity to track and store students’ learning activities as big data sets within online environments
Learning Analytics Methods in Education The following methods and analysis approaches inform faculty, educators, and administrators in higher education who are not experts in learning analytics (LA) about the available methods reported in the literature
Analysis of the literature provided a comprehensive overview of the analytical learning methods, benefits, and challenges regarding the use of big data in education

Summary

Introduction

The advancement of technology has provided the opportunity to track and store students’ learning activities as big data sets within online environments. Big data refers to the capability of storing large quantities of data over an extended period and down to particular transactions (Picciano, 2012). Users can take big data from different sources to include learning management systems (e.g., Blackboard), open. Similar to decision making driven by data, analytics refers to the scientific process that examines data to formulate conclusions and to present paths to make decisions (Picciano, 2012). According to Brown (2012), the process of systematically collecting and analyzing large data sets from online sources for the purpose of improving learning processes is called learning analytics (LA). Experts in online learning in American higher education predict that within the few years learning analytics will be widely used in online education to identify students’ pattern of behaviors and to improve students’ learning and retention rates

Methods

Results

Conclusion