Big Educational Data & Analytics: Survey, Architecture and Challenges

Abstract

The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

A Systematic Review of Questionnaire-Based Quantitative Research on MOOCs

“…A preferable approach may be to expand the scope and target of data collection, and establish a large-scale database in the MOOC field, perhaps even worldwide. This would serve to make the data sources more objective, more universal, and more convincing (Ang et al, 2020).…”

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

“…There is substantiated evidence in recent literature claiming that combining multiple machine learning classifiers would improve the prediction results, with prediction accuracy improvements ranging from 25% to 30% [16], [46], [47]. For instance, [48] integrated several classification algorithms to predict student performance through a voting mechanism.…”

“…Typically, data analytics of educational data involve two main strands, namely predictive analytics and learning analytics [15]. Predictive analytics seeks to predict student learning and performance, identify student failure rates, and recommend future courses that yield the best results [16]. However, learning analytics seeks to collect and analyze student learning data and their environment to improve the attainment of student learning outcomes [17].…”

“…The prediction of student performance in higher education is a worthwhile activity for it accomplishes strategic benefits, such as the development of early warning and course path recommendation systems, the detection of adverse student behaviors, and the automation of course assessment [13], [16], [26]. However, the accurate prediction of student academic achievements is a complicated research endeavor, requiring an in-depth understanding of all aspects and circumstances surrounding the students and their learning environment [9].…”

Learning analytics for higher education: proposal of big data ingestion architecture

“…In recent years, more universities start to use learning analytics to obtain findings on the academic progress of students, predict future behaviours, and recognize potential problems in an early stage [10] using the traditional database analytics, not on big data. In the context of big data in education, some specific big data architectures or framework has is proposed for education [11]. However, there are still limitations in adopting big data analytics architecture for enterprises as current frameworks provide generic architecture for big data analytics [12].…”