Learning Analytics in Higher Education

Abstract

It is known that big data has penetrated several if not all spheres of life. In higher education, the ability to take these large amounts of data and process it into something meaningful for academic decision making is commonly referred to as learning analytics. This chapter provides an overview of learning analytics and its importance, as well as identifying academic data sources, techniques used for learning analytics and prediction, and data visualisation techniques used to present analysis for better understanding and eventual decision making. It also includes a discussion of learning analytics frameworks for research and some identified research challenges.

Anomaly detection in the course evaluation process: a learning analytics–based approach

“…The traditional education environment, which happened to be teacher-focused, is now shifting toward student-focused. The success of education is measured through various benchmarks such as how the students’ continuous evaluation is conducted, learning outcomes of the courses, students’ retention and attrition rate in the study (Raghavjee et al , 2021; Ulfa and Fatawi, 2021). These measurements can be performed through learning analytics (LA) and educational data mining (EDM).…”

Using educational data mining to predict sub-Saharan African science, technology, engineering, and mathematics students’ academic performance: a systematic review.

“…LMS, such as Moodle, is an online learning platform that dispenses knowledge simultaneously, as online behaviour is stored in each student's log (Hasan et al, 2019). SIS stores related academic data, such as demographics and current or past academic performance (Raghavjee et al, 2021). Student data held by these systems may help HEIs understand the dropout rates, uptake of STEM fields, and overall learning tendencies.…”

“…The discussion explores the EDM process within these systems, highlighting its significance in extracting meaningful insights from educational data. Chweya et al, 2020;Coronel & Morris, 2016;Du et al, 2020;Hasan et al, 2020;Khedr & El Seddawy, 2015;Li & Xu, 2023;Liang et al, 2016;Lottering et al, 2020;Matsebula & Mnkandla, 2016;Meghji et al, 2018, p. 2;Raghavjee et al, 2021;Ray & Saeed, 2018;Romero & Ventura, 2013;Roy & Singh, 2017;Singh & Pal, 2020;Thakar, 2015;Vambe & Sibanda, 2016;Wanjau & Muketha, 2018;Zohair & Mahmoud, 2019) Justification for the Focus on EDM over Learning Analytics…”

“…It allows for creating powerful, engaging, flexible online experiences (Chweya et al, 2020). LMS collects massive amounts of data useful for analysing student behaviour (Raghavjee et al, 2021). HEIs are generating large amounts of data from their LMS and applications.…”

“…Education increasingly relies on technology, and vast amounts of data about student activities are available in the academic environment. Records of student activities, ratings, and interactions with teachers and other students are now collected from learning management systems (LMS), including Blackboard and Moodle (Lee & McLoughlin, 2010;Raghavjee et al, 2021). HEIs are starting to be more informed of the possibility of monitoring LMS data to improve teaching and learning quality (Miguéis et al, 2018;Pelletier et al, 2021).…”