Educational Big Data Research Articles

Empowering students through active learning in educational big data analytics

PurposeThis paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students’ data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape.MethodsThe study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking.ResultsThe paper offers insights into how the teaching strategy effectively enhances students’ data analysis and critical thinking skills. It investigates the specific impact of active learning on students’ engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines.ConclusionThe key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session’s effectiveness to accumulate in students’ personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era.

Bibliometric insights into data mining in education research: A decade in review

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.

Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches

Despite the explosion of interest in adaptive learning and learning analytics (LA) for higher education (HE), there has been relatively little research integrating educational approaches’ indicators to build an adaptive learning analytics model. Adaptive learning analytics (ALA) models have grown in favor of HE due to their claims of enhancing student learning outcomes, providing personalized learning paths, and allowing students to interact with course material at their own pace. With focus on using data to personalize the learning experience and the environment in which the experience of learning occurs, LA centers on enhancing education through meticulous data analysis, while big data (BD) in education addresses the overarching challenges and opportunities arising from extensive and varied datasets. These concepts are interconnected, where LA and ALA specifically apply BD principles within the educational context. In this paper, we explain some BD concepts used in HE, define the essential perceptions related to LA, and analyze educational approaches to define the fundamental implications. Besides, we try to connect some LA model with educational approaches based on the big educational data collected in order to establish an efficient educational model. We include all steps cited before we try to build an ALA model in HE that resolves the limitations of the oldest models, thus improving the learner’s learning process by adding and treating additional indicators.

Construction of E-Learning English Wisdom Classroom Based on Educational Big Data Mining

Construction of E-Learning English Wisdom Classroom Based on Educational Big Data Mining

Exploring school factors of academic achievement in Macau: The application of educational data mining in Progress in International Reading Literacy Study (PIRLS) 2016 assessment

International large-scale assessments, provide structured and static data, and due to their extensive databases, they can be considered as a valuable resource for Big Data in Education. In this paper, we propose an educational data mining approach to detect and analyze factors linked to academic performance in Macau schools using data from the Progress in International Reading Literacy Study (PIRLS) 2016. We conducted a secondary data analysis based on a set of socioeconomic, process, and outcome variables from PIRLS and other sources, and built decision trees to obtain a predictive model of school performance. By doing so, we were able to identify the school and student-level variables that are most significant in predicting student performance in Macau. These findings will be useful for informing educational policy decisions and shedding light on the causes of poor performance in Macau schools. Overall, our study highlights the potential of educational data mining approaches in analyzing large-scale assessment data and generating insights for educational research and practice.

Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

Hadoop-Based Political and Ideological Big Data Platform Architecture and Pattern Mining in Higher Education

There is a growing opportunity for self-improvement within the realm of higher education, represented by the combination of big data technologies and higher education. The Education Big Data Processing Framework (EBDPF) is the name of the platform that is the subject of this article, which focuses on the development of an advanced big data processing platform. In order to process huge datasets in an effective manner, the platform makes use of Hadoop's big data storage architecture, in addition to Hive, Flume, and Sqoop for data collection and synchronization, respectively. In this study, the implementation of standard data mining techniques is investigated through the use of MapReduce programming. More specifically, the study investigates the execution efficiency and scalability of these algorithms within the Hadoop platform. The data clustering task in data mining is chosen as a representative problem in this study so that the effectiveness of the EBDPF may be evaluated accordingly. An implementation of the clustering job that is based on MapReduce is created and tested on the Hadoop platform. Extensive experiments are conducted with a range of cluster sizes and data sizes. The findings provide more evidence that Hadoop distributed systems are more efficient and successful than traditional methods when it comes to managing data mining applications. The extended performance analysis of computing power not only provides evidence that the platform is currently effective, but it also provides a signal that it has a significant amount of potential that has not yet been utilized. At the convergence of big data technology and higher education, the EBDPF emerges as a strong and promising framework, demonstrating considerable steps in accelerating data processing within educational contexts. In conclusion, the EBDPF is a framework that demonstrates significant progress.

The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

“The words educational data mining and learning analytics are frequently used interchangeably, despite their being an increase in their investigation and implementation. This may be as a result of the fact that both areas have similar conceptual components. One way to ensure precision, homogeneity, and consistency It aims to pinpoint themes that are similar to and different from one other in the two domains as they develop. This a topic modelling study of papers on educational data mining and learning analytics was carried out in the elucidate the two areas' respective themes. In particular, we used structural topic modelling to find the two domains' subjects from the abstracts. For instructional purposes, we use structural topic modelling on N 1 4192 articles. For both educational data and survey data, we infer five-topic models analytics for mining and learning. While there may be disciplinary variations in research, our findings show that beyond their various lineages, there is no evidence to indicate a clear separation between the two disciplines. the area of educational research on the uses of advanced statistical methods is trending toward convergence for improving teaching and learning, discover how to mine massive data streams for insights that may be put to use. Over the past five years, both areas have converged on a growing emphasis on student behaviour. This study topic has advanced greatly, and a variety of related words, including Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science, are now used in the paper. The main publications, significant turning points, cycle of knowledge discovery, primary educational settings, specialised tools, freely accessible datasets, widely used methodologies, primary goals, and anticipated trends in this field of study are reviewed to provide the state of the art at this time.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

Research on Intelligent Resource Recommendation and Evaluation Mechanisms in the Field of Media Education Technology

Abstract The development of technologies such as educational big data, artificial intelligence and adaptive learning provides technical support for the realization of intelligent learning resources. In this paper, the traditional collaborative filtering algorithm is improved to get an intelligent hybrid recommendation algorithm through a hybrid interest model, and the evaluation mechanism of teaching resources is realized by using the hierarchical analysis method. The intelligent resource recommendation and evaluation mechanism model in the field of media education technology has been constructed, and the media education teaching system is designed to assist students in their learning. The results of the system test show that the NDCG index (52.9%) of the intelligent resource recommendation model in this paper is improved compared to the RKGE model, and it can achieve effective teaching resource recommendations. It was also found that students in the experimental class with the application of the intelligent resource recommendation and evaluation system to assist teaching have a higher degree of participation in independent inquiry learning activities, and their learning ability is better than that of the control class under traditional teaching. The students in the experimental class had a mean satisfaction score of 4 or more on the resource recommendation system, considered the learning resources provided by the model useful, and were willing to use and share the learning system. The intelligent resource recommendation and evaluation mechanism proposed in this paper can be an effective way to meet learners’ media learning needs and improve their learning effect and interest.

Research on the Application of Data Mining in Educational Big Data

Research on the Application of Data Mining in Educational Big Data

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Research on the Theoretical Innovation of Civic and Political Education in Colleges and Universities Based on Big Data Technology

Abstract In this paper, the EKPCA algorithm with a better clustering effect is obtained by improving the metric distance formula of the K-prototypes algorithm, which is combined with the XGBoost algorithm with integration ability to construct a method for analyzing students’ ideological and political behavior. After verifying the validity of the method, the method is used to analyze students’ comprehensive quality, academic performance mental health status, etc., to help the innovation of ideological education. Students’ comprehensive quality assessment analysis scores on average fall within the range of 67-68 points, which is below average. There is a 3.5-point difference between the grades of 2022 freshman students and those of 2021 freshman students, and the average number of failed courses is twice as low as before. The decisiveness and cowardice score of male students is 5.28 points higher than that of female students which is 1.31 points, and the score of male students in terms of extroversion and introversion is 2.85 points higher than that of female students, male students are more extroverted and bold than female students, and more attention should be paid to female students. We aim to actively strengthen the guiding position of Marxist theory in Civic and Political Education, while also promoting the integration and innovative development of big data in Civic and Political Education.

Practice of Artificial Intelligence Technology Based on Big Data in Football Training and Physical Education

Practice of Artificial Intelligence Technology Based on Big Data in Football Training and Physical Education

Educational big data mining: Mediation of academic performance in crime among digital age young adults

The connection between academic performance and crime has been proven in many studies, but there is a lack of recent literature documenting academic performance as a mediator between predictors and crime, especially in a large population that has grown up in the digital age of which the high frequency of communication exchange differentiates it from previous times. The present study aims to discover the mediating role of academic performance in the connections between each predictor (the Internet usage, biological sex, duration of sleep, fast food intake, medical history, alternate drug use, and illegal drug use) and crime using a large data set from the national longitudinal study of adolescent to adult health (>90,000 sample size). The bivariate correlation between each predictor and crime is presented, as well as the direct and indirect effects for each predictor on crime through academic performance as the mediator. The results highlighted that there is a significant relationship between the Internet usage with crime, a negative correlation of academic performance with crime and positive correlations of fast food intake, alternative medicine use, and illegal drug use with crime. In terms of mediation analyses, academic performance mediated the relationship between all the predictors (except for duration of sleep) and crime. Results may aid education policy makers in deterring crime in the digital age by improving academic performance while targeting its predictors.

Online education big data mining method based on association rules

Online education big data mining method based on association rules

Educational Big Data Analytics: Machine Learning Based Academic Performance Predictive Modelling

Educational Big Data Analytics: Machine Learning Based Academic Performance Predictive Modelling

Research on the Optimization Path of College Education and Teaching Based on Big Data Analysis

Abstract Educational big data analytics can provide data support and decision-making reference for the optimization of education and teaching in colleges and universities by mining the daily behaviors of college and university students, searching for the data features that affect college students’ academic performance, and establishing corresponding prediction and early warning models. In this paper, we constructed a college student performance prediction model based on the CNNBiLSTM-Attention algorithm, integrating the advantages of bi-directional long- and short-term memory neural networks and attention mechanisms, introducing the 1-tanh function after the forgetting gate to make the output value of the forgetting gate in a more obvious range, retaining the features of the input data as much as possible, and improving the learning ability of LSTM. In the context of teaching in colleges and universities, we observed that the model in this paper achieved an accuracy of 86.39% and an F1 value of 83.59% on two dataset tasks, Principles of Microcomputer and News Editing, respectively. When predicting the results of the 2022 academic year grade performance points, the prediction accuracy of seven different score levels exceeded 80%. Three students in the experimental class had midterm scores below 40, but after teacher communication and targeted counseling, there were no students with scores below 40 at the end of the semester. In the midterm prediction, no student scored higher than 90. After the teachers adjusted the teaching plan and optimized the teaching arrangement based on the prediction results, six students had scores above 90 at the end of the final term. Students’ scores in all subsections have improved, and the overall distribution of scores has moved significantly toward the higher-scoring sections. From 68 points in the midterm prediction to 72 points in the final test, the number of sub-segments increased the most. The use of big data technology has had an optimizing effect on existing universities’ traditional education and teaching models.

Practical Exploration of Big Data Technology in Improving the Efficiency of English Education Resource Allocation and Utilization

Abstract In order to make full use of educational big data for English education resource allocation and utilization, this paper is based on the standard Bottle Sheath Optimization Algorithm, which generates a random number through the sine-cosine function to update the random positions of individual Bottle Sheaths to improve the randomness and population diversity of the leader search process, improve the Bottle Sheath position updating method, and enhance the algorithm’s ability to perform global search. This paper develops a resource allocation model that can be employed for English education resource allocation. For the vast majority of single-peak and multi-peak functions, the success rate of SCSSA’s solution is 100%, and the performance of SCSSA’s search is significantly better than that of the comparison algorithms (p<0.05), and all of its convergence curves have the fastest decline. After using the model for allocation, the student-teacher ratio of the five districts and counties was improved, with a cumulative decrease of 86.72% in the student-teacher ratio, and the gap in the student-teacher ratio between districts and counties was reduced. However, the model’s optimization effect on the allocation of English books is limited, suggesting that further support of human and financial resources is needed in the allocation process.

Educational Big Data Analytic – A Mediation Analysis of the Covariates of Academic Performance

Educational Big Data Analytic – A Mediation Analysis of the Covariates of Academic Performance