Research on Educational Data Mining Based on Big Data

Author(s):  
Xuping He ◽  
Wensheng Tang ◽  
Jia Liu ◽  
Bo Yang ◽  
Shengchun Wang
Author(s):  
Areej Fatemah Meghji ◽  
Naeem A. Mahoto

In higher education, the demand for improved information in relation to educational and learning outcomes is greater than ever before. Leveraging technology, new models of education have emerged that are not only improving modes of lecture delivery and information retention, but also generating huge amounts of data. This data is potentially a gold mine that needs to be explored to uncover patterns associated with student behavior and how information is processed, retained and used by the students. This chapter proposes a generic model that uses the techniques of educational data mining to explore and analyze Big Data being generated by the education sector. This chapter also examines the various questions that can be answered using educational data mining methods and how the discovered patterns can be used to enrich the learning experience of a student as well as help teachers make pedagogical decisions.


Author(s):  
Shakir Khan ◽  
Salihah Alqahtani

Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.


Author(s):  
Samira ElAtia ◽  
Donald Ipperciel

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sumeer Gul ◽  
Shohar Bano ◽  
Taseen Shah

Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.


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