Learning Analytics and Education Data Mining in Higher Education

Introduction

In this chapter, we present a general survey or overview of the use of Learning Analytics (LA) and Educational Data Mining (EDM) as they relate to higher education. We aim to provide potential answers to how both LA and EDM can advance higher education institutions from a large-scale, big data educational research perspective. For both cognate disciplines, data generated by and available in universities provide a fertile ground for carrying breakthrough research and analyses in understanding and advancing education. This data covers a wide and diverse range of contexts: finances, human resources, the Registrar’s Office, maintenance and facilities, internet use, admission, program development, libraries, research, and teaching and learning. In particular, universities are heavily scrutinized for the training and skills they provide to students. Consequently, over the past few decades, universities have been paying close attention to these issues, using tools available through advances in big data analysis, with employability and graduate skills the focus for many institutional programs. For the teaching, learning and services provided to students, it seems that both EDM and LA can become optimal tools to guide universities in adapting to these changes and addressing specific needs for the future.

EDM emerged as a field of its own using data mining techniques in educational environments. There are a variety of methods and applications in EDM that can be classified into two categories. On the one hand, EDM can be used for applied research objectives such as enhancing learning quality. On the other, it can be used as a tool for pure research objectives, which tend to improve our understanding of the learning process (Bakhshinategh, Zaiane, El Atia & Ipperciel 2018). In a multifaceted environment in which data come from different sources and in different forms, EDM offers a wealth of possibilities for understanding and shaping learning (Cios 2007, ElAtia et al. 2012; Hammad, 2009). These techniques are eclectic in nature and combine qualitative and quantitative research approaches. They also enable researchers to analyze big data that are affected by many unknown variables. Han & Kamber (2006) defined data mining as the “analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owners. (p. 5)” Data mining is a multidisciplinary field that involves methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

LA is data analytics in the context of learning and education. It involves collecting data about learner activities and behaviours, as well as educational environments and contexts, using statistics and data mining techniques to extract relevant patterns that reveal how learning takes place. LA can be used (1) to report measures and patterns related to learning activities or (2) to optimize learning strategies and environments. Educational applications of EDM and LA in higher education are an emerging and growing trend resulting from the vast amounts of data becoming available from the growing number of courses delivered in e-learning and hybrid environments. They are a powerful means to systematically analyze the large amount of data produced by institutions of higher education. More on this below.

In the following sections, we engage with the reader in various discussions that are central to LA and EDM from a social-science perspective. To do so, in the first two sections entitled “Potential and Promises of Big Data in Higher Education” and “KDD, Data Warehousing and EDM,” we present various tasks and applications that already exist in the field of EDM and LA in higher education and are seen as promising. For the second part, we focus primarily on presenting an overview of various “Stakeholders and Uses of EDM and LA” to brush a broader picture of EDM and LA in the context of their application.” We categorize uses based on their purpose, uses, and impact on various stakeholders. Then, we engage in a discussion of the issues around ethical data uses and privacy in the section entitled “Privacy, Confidentiality and Ethical Consideration,” and we conclude by contributing to a nascent discussion “At the Intersection of Two Fields” that bridge LA, data mining and higher education. In this context, we explore the possible barriers hindering a wider adoption of EDM and LA, whether as a consequence of ethical considerations or the difficulty to fulfil their interdisciplinary nature.