Mining Big Data in Education: Affordances and Challenges

Abstract

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

“…Such descriptions of student behavior, using various visualization and exploratory data mining techniques, were the focus of the earliest research in educational data mining (e.g., Baker and Yacef, 2009;Romero and Ventura, 2007). In recent years, the uses of clickstream data in educational research have expanded far beyond simple descriptions and have introduced both the possibility of empirical examination of educational theories using fine-grained process data and a new wave of data-driven pedagogical interventions (Fischer et al 2020). The direction of these advances can be categorized into three main groups.…”

An Intelligent Recommender System for Personalized Test Administration Scheduling With Computerized Formative Assessments

“…As an advanced method for exploiting big data, the IRS can accurately predict when data-driven actions, such as test administration decisions, should be taken for individual students. This is a good example of using big data for extracting actionable knowledge in education (Fischer et al, 2020).…”

“…With the increased availability of big data in education, advanced data analytic approaches offer promising solutions to the daunting task of individualizing student formative assessment schedules in meaningful ways (Dede, 2016;Fischer et al, 2020). IRSs are an example of advanced data analytic approaches, which use all of the available data to produce optimal recommendations for users.…”

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

“…It must also be considered that some universities or schools might not have professional IT workers on sight for handling any situation. With the help of fog computing, the trained IT personnel can perform their tasks and manage the system remotely [33]. Content retrieval availability in educational systems have encountered a number of issues related.…”

“…There is a number of factors to criticize big data analytics utilization issues in higher education, where fog computing is implemented [33]. The first issue is based on the absence of data as a result of countless events, particularly in educational classes and communications with instructors and students.…”