Educational Big Data Research Articles

Educational Big Data Initiates a New Era in Education Development

Educational Big Data Initiates a New Era in Education Development

Big Data in Higher Education: Bibliometric Analysis

Big Data in Higher Education: Bibliometric Analysis

Utilizing Big Data to Create Educational Information Systems

Background and Aims: Using big data to create educational information systems is critical for customizing learning experiences for individual students while also improving overall educational outcomes. Using data-driven insights, educators and administrators can make informed decisions to improve teaching strategies, predict student performance, and streamline operations, resulting in a more effective and personalized educational experience. The purpose of this paper is to investigate the use of Big Data to create Educational Information Systems. Methodology: The methodology calls for the use of a variety of data sources, including academic journals and case studies, as well as a structured literature review protocol to ensure comprehensive coverage. Data is collected and analyzed thematically to identify key trends, applications, and challenges in big data utilization in education, resulting in a comprehensive overview of its impact. Results: The study discovered that the review emphasizes big data's transformative potential in educational information systems, specifically its ability to improve personalized learning, predictive analytics, curriculum development, and administrative efficiency. Successful implementations, such as those at Georgia State University and Purdue University, highlight the significant benefits of using big data while emphasizing the importance of addressing ethical and privacy concerns. The future of big data in education promises even greater advances thanks to technologies like AI and real-time analytics, but it also necessitates ongoing research into ethical frameworks and their impact on educational equity. Balancing innovation with privacy and ethical concerns will be critical as educational institutions, administrators, and policymakers navigate these changes, allowing big data to reach its full potential while maintaining equitable and effective educational practices. Conclusion: The article emphasizes big data's potential to transform educational systems through personalized learning, predictive analytics, curriculum development, and administrative efficiency. Moving forward, it is critical to strike a balance between technological advancements and ethical and privacy concerns to ensure that big data innovations are used effectively and fairly in education.

MODEL OF STUDENT PREPARATION IN HIGHER EDUCATION IN THE CONTEXT OF BIG DATA

Developing a robust educational system across all domains is essential for providing contemporary educational programs. The aim of our research work is to identify and practically implement the theoretical foundations of preparing students in the field of big data in higher education, which is the cornerstone of training amodern, competitive future specialist.Ensuring the high-quality organization of fundamental disciplines is crucial in developing specific competencies for future educators. Therefore, the main focus of the proposed article is on shaping an educated individual with a broad outlook and high culture of thought in preparing IT specialists at the L.N. Gumilyev Eurasian National University. The article provides a comprehensive definition of the model of student preparation in higher education in the context of big data, outlines and implements components, and discusses the model for achieving positive outcomes. Before considering the model for improving student preparation in the context of big data, let us briefly define big data and examine the concept of a model.

Mining educational big data to develop an early alert dynamic model of academically at-risk students: A proof of concept

ABSTRACT Educational big data has great predictive power to identify academically at-risk students. Based on fully mining educational big data, this study developed an early alert dynamic model of academically at-risk students to confirm and extend this proposition. An empirical test was carried out to validate the reliability and validity of the dynamic model by using data analysis methods. The results proved that the developed model could precisely identify academically at-risk students, scientifically assess their academic risk status, and dynamically provide personalised interventions. The overall prediction success rate of the developed model was 80.8%. In addition, application reflections were discussed to shed light on future implementation.

Harnessing Educational Big Data Analytics for Decision-Making in Enhancing School Teaching Quality

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality.

A study of online academic risk prediction based on neural network multivariate time series features

SummaryNeural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

Benefits of Big Data in Supporting Better Educational Decision Making

Big data plays an increasingly important role in education, offering great potential to improve educational decision making. Big Data collects, stores, and analyzes large amounts of and diverse data at very high speeds. In the educational context, big data collects data from various sources, including school management systems, student academic records, student satisfaction surveys, and online data. Analysis of this data can provide valuable information to decision makers in the education sector to identify relevant trends, patterns and opportunities. This research discusses the great benefits that can be gained from using Big Data in an educational context to support better decision making. Through research on the educational benefits of big data in supporting better decision making, it is hoped that this will provide an excellent opportunity to improve decision making in education. Through careful data analysis, education can become more effective, efficient and relevant to better meet the needs of students and society. The method used in this research is a quantitative method. Researchers conducted a survey using a Google form consisting of 15 statements related to the title of the research. Researchers found that using big data in education provides great opportunities to improve better educational decision making. And supported by careful selection as material for consideration. The limitation of this research is that the researcher only conducted research in schools and the researcher did not conduct research directly in schools but shared a survey link on a Google form containing a statement about the benefits of big data in supporting better educational decision making.

Educational Big Data Analytics Using Sentiment Analysis for Student Requirement Analysis on Courses

Educational Big Data Analytics Using Sentiment Analysis for Student Requirement Analysis on Courses

Big Data in online education

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.

Precisely identifying and mitigating shallow reading behaviors through online reading data: exploring quantitative reading behavior methods and precise behavioral intervention

ABSTRACT Online learning has become a part of daily life. However, it has fostered a tendency to shallow reading. Notably, little attention has been paid to precisely identifying shallow reading behaviors using quantified education big data in favor of methods such as conceptual descriptions, opinions, and questionnaires, resulting in an imprecise analysis of its impact on learning and a lack of strategies to mitigate its causative factors. This study addresses the issue by introducing a behavior-identifying model grounded in measurable data to precisely identify shallow reading behaviors. Initially, three key dimensions of shallow reading behaviors were proposed, along with corresponding mathematical indicators. Subsequently, the k-means algorithm and Pearson correlation coefficient were employed to evaluate the relationship between shallow reading and learning performance, underscoring its significance. The analysis showed that shallow reading behaviors are highly associated with poor learning performance, thus hindering critical reading. These findings further validate the predominant influence of reading speed on learning performance. Consequently, a strategy was developed to intentionally decrease reading speed. Following that, an experiment was conducted to assess the effectiveness of this strategy in mitigating shallow reading. The results revealed improved learning performance and a significant reduction in shallow reading behaviors without increasing cognitive load.

Uncovering insights from big data: change point detection of classroom engagement

Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and teachers’ teaching styles by analyzing educational big data. In the analysis, data from 120 students and two mathematics class teachers at a public junior high school in Japan were used. We applied the pruned exact linear time (PELT) algorithm to automatically identify significant changes in student participation during class. Based on the information obtained, we analyzed the interaction logs of teachers’ e-book readers and clarified the relationship between student participation rates and teacher behavior patterns. Change point detection using the PELT algorithm showed a high F1-score of 0.7929, indicating good overall performance. We also investigated whether there was a relationship between class differences and teachers’ actions and found a statistically significant difference. The results provide clues for improving student learning engagement and teachers’ teaching styles, and they are expected to improve the quality of education by automatically identifying notable cases from educational big data. However, further research is required to improve data analysis methods, such as adjusting the parameters of algorithms based on the situation.

Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.

Educational Big Data Analytics for Futuristic Smart Learning Using Deep Learning Techniques

The goal is to use the massive amounts of data created by digital education systems to develop intelligent and adaptable learning environments that are particularly suited to each student’s requirements. The rapid digitization of education systems has led to the proliferation of educational big data, presenting unprecedented opportunities to reshape learning environments into intelligent, responsive spaces that adapt to the needs of individual learners. This paper explores the integration of advanced deep learning techniques with educational big data analytics to forge the path towards futuristic smart learning ecosystems. By leveraging robust datasets derived from a myriad of educational interactions, ranging from student performance metrics to engagement patterns in digital learning platforms, we propose a multi-tiered analytical framework that harnesses the predictive power of deep learning. We commence by elucidating the scope and scale of educational big data, highlighting its potential to provide granular insights into student learning processes. The paper then delineates the architecture of a deep learning-based analytical model designed to process complex, multidimensional educational datasets. This model applies state-of-the-art algorithms to perform tasks such as predictive analytics for student performance, personalized content recommendation, and real-time engagement monitoring. Central to our discussion is the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) in deciphering patterns and trends that escape traditional analytical methodologies. We emphasize the capacity of these techniques to capture the subtleties of learner behavior and to facilitate the development of adaptive learning pathways. Furthermore, we address the challenges of integrating deep learning with educational big data, including issues of data privacy, computational demands, and the need for robust model interpretation. The paper presents a series of case studies that demonstrate the successful application of our proposed framework in various educational settings, from K-12 to higher education and continuous professional development.

Optimizing Large-Scale Educational Assessment with a "Divide-and-Conquer" Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models.

With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational speed. This study introduces a novel "divide- and-conquer" parallel algorithm built on the Wasserstein posterior approximation concept, aiming to enhance computational speed while maintaining accurate parameter estimation. This algorithm enables drawing parameters from segmented data subsets in parallel, followed by an amalgamation of these parameters via Wasserstein posterior approximation. Theoretical support for the algorithm is established through asymptotic optimality under certain regularity assumptions. Practical validation is demonstrated using real-world data from the Programme for International Student Assessment. Ultimately, this research proposes a transformative approach to managing educational big data, offering a scalable, efficient, and precise alternative that promises to redefine traditional practices in educational assessments.

Opportunities and Challenges of College Mental Health Education from the Perspective of Big Data

This paper explores the opportunities and challenges of college mental health education from the perspective of big data. Firstly, through literature review, the importance of mental health education and the current issues are elucidated. Then, from the perspective of big data, the potential opportunities of big data in college mental health education are analyzed, including data-driven personalized education, real-time monitoring and warning systems, and interdisciplinary research and collaboration. At the same time, the challenges faced by college mental health education under the perspective of big data are also pointed out, such as data privacy and security issues, insufficient data analysis and interpretation capabilities, and inadequate technical facilities and talent support. Lastly, the research content of this paper is summarized, and directions and suggestions for future research are proposed.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

Self-efficacy of Data Literacy among Primary and Secondary School Teachers in Guangdong Province

The application of educational big data provides new basis for teachers to improve teaching and promote student development. However, there are still many challenges in using data to solve complex educational problems. Teacher data literacy has become the key to transforming educational data into effective teaching strategies. The study sought to gain a better understanding of the current state of primary and secondary school Teachers self-efficacy of data literacy and put forward some suggestions for teacher data literacy training. This study used a quantitative methodology. 813 primary and secondary school teachers from Guangdong Province, China, completed a questionnaire on self-efficacy of data literacy. (1) Primary and secondary school teachers reported significantly high levels of self-efficacy in data literacy (mean=3.624). Among the dimensions, the data awareness and ethics dimension had the highest self-efficacy (mean=3.738) and the data research dimension had a lower self-efficacy (mean=3.358). (2)There were significant differences in the primary and secondary school teachers’ self-efficacy of data literacy with different ages, educational backgrounds, and school locations. Primary and secondary school teachers generally recognized the importance of data literacy training. Older teachers who worked in rural schools and had low levels of education have the lowest level of self-efficacy for data literacy. For primary and secondary school teachers' low level of data analysis skills and weak data transformation strategy ability, teacher data literacy training adopts an embedded teamwork approach, integrating multiple resources to build a data team, integrating big data technology throughout the entire teaching process, and helping teachers to use new data and technologies to understand students' needs in a timely manner, to select teaching methods, and to solve real-world problems. It makes data literacy the foundation of teachers' teaching and promotes the development of all students.

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs).