Educational Big Data Mining: The Analysis of Academic Performance Impacts on Quality of Life

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

In recent years,research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

Educational Big Data Mining: The Analysis of Academic Performance Impacts on Quality of Life

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.

In the digital era, enormous amounts of data have been generated in education that has led to data driven approaches which in turn help effective decision making. Educational data mining has been used as a very effective tool for identifying the hidden patterns in educational data, predicting students' performance and to enhance the teaching /learning environment. Educational data mining tools and techniques help institutes in providing information about students e.g., about enrolment of students, weak students can be identified earlier so that various corrective strategies can be applied, and various resources can be allocated to enhance their performance and their success in courses they enrolled. This paper examines the research efforts that have been made in the field of educational data mining and the various educational data mining tools and techniques used in recent years for predicting student's performance. We live in a world where enormous volumes of data are collected, but if these data are not further examined, they remain nothing more than enormous amounts of data We may use this data, analyze it, and gain a significant edge by using new approaches and procedures. Data mining is the ideal approach in this situation. Extraction of hidden and valuable information and patterns from massive data sets is known as data mining. It has already been widely used in several fields, including banking, sales, marketing, telecommunications, and finance. This essay aims to introduce a unique use of data mining for education, known as educational data mining. An interdisciplinary study topic called Educational Data Mining (EDM) was established to apply data mining to the educational sector. To examine the data gathered during teaching and learning, it employs a variety of tools and methodologies from machine learning, statistics, data mining, and data analysis. The process of turning large educational databases' raw data into useful information that can be used for decision-making in educational systems as well as for a better understanding of students and their learning circumstances is known as educational data mining.

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

With the new era in information technology, Educational Informationization not only profoundly affects the modern education paradigm, but also provides strong technological feasibility to facilitate and realize education reform. However, the ubiquitous accessibility brought about by today's internet and wireless technologies creates explosive increase in educational related data (big data) from a diversity of resources. It also becomes much more challenging to analyze and understand the underlying meaning of the big data. It is high time that the modern education systems should embed data mining into education paradigm and apply decision analysis to guide teaching strategy. It is our belief that Education Data Mining (EDM) is vital to achieve such educational innovation. This paper proposes a novel Online System for Correcting and Analyzing Exam Paper (OSCAEP) based on a combination of EDM and Network Informatization, which is aimed to provide in-depth analysis of exam papers, to construct an innovative EDM model and to study the interrelationship between educational factors. (1) First, we introduce the background, significance, and purpose of our research. We illustrate OSCAEP from three perspectives: $\pmb{a}$. Educators: For educators, they can get an insight into potential problems from the quantitative results by data mining; $\pmb{b}$. Learners: For learners, they can understand their learning progress from various angles; $\pmb{c}$. Administrators: For administrators, they can timely redesign the education strategies based on such quantitative feedbacks. (2) Second, we present the innovations of OSCAEP from theoretical part and technological part. OSCAEP also include some advanced teaching theories such as Personalized Learning, Precision Teaching and Customized Teaching. Our main technological innovation hinges upon integrating several machine learning data analysis techniques (e.g., K-means, DNN) to train scores and obtain the classification rules. (3) Third, Our presentation of OSCAEP contains two major parts: $\pmb{a}$. build a distributed multi-layer hardware architecture, which includes web server, application server cluster and management workstation, and $\pmb{b}$. provide a multi-stage pipeline software model, which expands the traditional process of data mining into six stages (Data Preparation, Data Filtration, Data Pre-processing, Data Conversion, Model Building and Data Training). From the application perspective, we develop an effective model for examination analysis and learning behavior prediction based on education big data. Examples include statistical score data of different types of questions (Blank, True/False, Choice and Coding). (4) Forth, OSCAEP also offers educators some potentially very valuable suggestions and advices. It is found that the learning performance has no direct relation with the learning time, but it has much to do with the learning habits. With the EDM analysis results, educators can better customize teaching and design efficient rules and regulations in accordance with learner's features. This leads to precision teaching and plays a vital role for a more desirable atmosphere for learning and teaching. In short, OSCAEP processes information in a digital, automatic, intelligent and networked way. As such, it provides more precise and timely information than the traditional approach.

PurposeWith the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.Design/methodology/approachBig data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.FindingsBig data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.Research limitations/implicationsThis research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.Originality/valueOver the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

The article discusses the problem of managing the effectiveness of a teacher's professional activity in the context of an increase in the speed of the processes of generating data on education in a digital educational environment. The article presents a procedural model of organizing management of the effectiveness of the teacher's professional activity based on work with big data in general education, developed and approved by the Nizhny Novgorod Institute of Education Development, which is a basic component of an in-school system for assessing the quality of education, focused on the implementation of systematic work with different types and composition of data based on complex competences of teachers (professional and digital) in the field of Big Data in education. Introduced into the scientific circulation of the theory and methodology of vocational education, the concept of "complex professional competencies" in relation to teachers working with big data in education, the formation of which is a prerequisite for the implementation of effective professional activities of a teacher in a modern school. Within the framework of the project study, focused on testing the hypothesis about the conditions and mechanisms for managing the effectiveness of the teacher's professional activity in the new conditions, the main approaches to the formation of complex competencies of the teacher in the field of Big Data in education have been identified, problem areas in the teacher's activities have been identified that complicate the process of their formation and improvement and possible ways to eliminate them, a list of indicators of the effectiveness of the teacher's professional activity in the field of Big Data in education has been determined. The practical application of the developed procedural model for managing the effectiveness of the teacher's professional activity is important for creating conditions in a modern school for educational activities that fully satisfy the needs and demands of all participants in educational relations.

With the rapid development of the Internet and communication technology, online education has drawn more and more attention, online learning platforms, on the other hand, store massive learner behavioral data and educational data. How to effectively analyze and utilize the data to improve the quality of online education has become a key issue urgently needed to be solved in the field of big data in education(BDE), educational data mining(EDM) is exactly an effective and practical method and means of applying BDE. Therefore, EDM is an important academic research hotspot in the field of EDM. Firstly, the paper introduces the basic concepts of BDE, EDM and online learning platform, and then elaborates on the process of how educational data mining transforms raw data into knowledge. Finally, the key technologies of data mining are classified according to their uses, and gives its application in online education scene. The paper can provide some guidance for the research and application of educational data mining based on online education.

Today, most educational institutions have become more interested in big data. Because the importance of extracting useful information from educational data to support decision-making on educational issues has increased day by day. In this context, through educational data mining, this research study aims to reveal the association rules among compulsory courses in the Computer Education and Instructional Technology curriculum within the faculty of education of a state university in Turkey. In this context, the research was conducted with data obtained from 258 preservice teachers who had completed all of their compulsory courses (n = 42) for the Computer Education and Instructional Technology curriculum, having graduated from the Computer Education and Instructional Technology program between 2012 and 2020. According to the experimental results, the academic performance of preservice teachers in some courses could be used as a predictor of their academic performance in other courses. Other findings from the study are discussed in detail, and suggestions put forth for future research.

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.This article is categorized under:Application Areas > Education and Learning

Accurately predicting student performance and identifying anomalies in academic datasets has become increasingly crucial for enhancing educational outcomes and enabling data-driven interventions in modern learning environments. Traditional statistical methods and conventional machine learning approaches often struggle with the multidimensional nature and increasing scale of contemporary student datasets, which encompass diverse academic, behavioral, and socio-demographic variables. This study explores advanced deep learning techniques; including Autoencoders for unsupervised anomaly detection, Recurrent Neural Networks with Long Short-Term Memory architectures for temporal pattern recognition, and Deep Neural Networks for comprehensive performance prediction to address these challenges. The proposed framework demonstrates significant improvements in detecting subtle performance anomalies that often precede academic difficulties, while simultaneously predicting longitudinal success patterns with greater accuracy than traditional methods. By leveraging the hierarchical feature learning capabilities of deep architectures, our system enables early identification of at risk students through continuous analysis of complex, nonlinear relationships in educational data, allowing institutions to implement timely, personalized interventions. Research studies have empirically validated the effectiveness of these models in educational contexts, showing superior performance in measuring student achievement patterns and predicting learning outcomes. The findings contribute to theoretical advancements in educational analytics but also provide practical insights for curriculum designers and policy makers seeking to optimize instructional strategies. Furthermore, the study establishes significant benchmarks for educational contexts by demonstrating how deep learning can enhance both teaching methodologies and student support systems through data-driven insights. This research makes a substantial contribution to the growing field of Educational Data Mining by proposing a robust deep learning framework that serves as both a predictive tool and a baseline for future studies in student performance analysis, while also addressing critical challenges in model interpretability and implementation scalability within real-world educational settings. References Abatal, A., Korchi, A., Mzili, M., Mzili, T., Khalouki, H., & Billah, M. E. (2025). A comprehensive evaluation of machine learning techniques for forecasting student academic success. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 7(1), 1-2. Acharya, A., & Sinha, D. (2014). Early prediction of students performance using machine learning techniques. 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Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations, RNN models can improve on the performance of these existing methods, in this work we take a different approach. Rather than directly comparing RNNs with other models, we are instead interested in the results when RNNs are combined with one of these existing models. In particular, we attempt to improve the performance of ALEKS (“A ssessment and LE arning in K nowledge S paces”), an adaptive learning and assessment system based on Knowledge Space Theory, through the use of RNN models. Using data from more than 1.4 million ALEKS assessments, we first build an RNN classifier that attempts to predict the final result of each assessment. After verifying the accuracy of these predictions, we develop our stopping algorithm, with the goal of improving the efficiency of the ALEKS assessment by reducing the total number of questions that are asked. Based on this stopping algorithm, we give a comprehensive analysis of the possible effects it would have on students. We show that the combination of an RNN with the ALEKS assessment can reduce the average assessment length by over 26%, while a high degree of accuracy is maintained.