Review on Application of Data Mining Educational Big Data

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 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.

Educational Data Mining (EDM) is an emerging, promising and interdisciplinary research area, which has an immense impact on student’s academic performance by analyzing educational data for improving teaching, learning experiences and, of course improving the institutional effectiveness. One of the most key facts in educational data is the significant growth of data and these statistics are rising swiftly without any benefit to the institution. In this short study, different studies conducted on the theme “EDM and interdisciplinary” were consulted and examined. Most of the authors have conducted studies on different systems which mostly include e-learning systems, student performance recommender systems (SPRS), conventional educational systems, adaptive and intelligent web based educational systems. The study of the past research has revealed that immense progress has been done in various domains of education and in different phases as well, but there still exists reasonable gap which needs to be examined in details as a future research study. Keywords: Educational Data Mining; Neural Networks; Classification; Association rules; Clustering; Prediction; k-means.

Chapter 13 - Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors

The availability of big data sets on the different learning modes has stimulated the applications of big data technology in education. This article is to introduce relevant application examples of education data mining to show how big data in education help to solve education issues. Firstly it lists the groups of target users and participants involved in education data mining, and the objectives to use data information of each group. It then goes on to illustrate the groups and their objectives further by given examples and finally conclude how big data help to solve education issues through reaching the objective of each group. The examples show that for students big data can generate personalized activities and offer academic advising, for teachers big data can reveal students' characteristics and predict future performance and offer assistance, for educational organizations big data can make scientific decisions.

Big data in education is of great significance to enhance students’ learning efficiency and improve teachers’ teaching quality, and is an important application of big data technology in the field of education.The purpose of this paper is to develop a Python-based educational data mining and learning analysis system.The advantages and shortcomings of educational data mining technology in the field of education are illustrated through the comparison of traditional education and online education and the application of related technologies at home and abroad.The basic framework of educational data mining and learning analysis system and the process of educational data mining and analysis are described, hoping to provide a reference for the research of online education system.

Big data has become a new driving force for national innovation and entrepreneurship. Although colleges and universities, which are responsible for cultivating high-quality entrepreneurship and innovation talents, have achieved certain results, there are still many problems in practice, which need to be driven by big data. Big data and the practice of innovation and entrepreneurship education in colleges and universities have certain inherent commonality. The integration of big data into the practice of innovation and entrepreneurship education in colleges and universities needs to be improved and strengthened in terms of top-level design, data environment, and educational concepts. Educational big data has become a hot topic and trend in educational research. College students’ entrepreneurship is a kind of entrepreneurial process in which special groups of college students and graduate students are the main body of entrepreneurship. With the recent transformation process of our country and the increasing pressure of social employment, entrepreneurship has gradually become a career choice for college students and graduates. Through data mining, statistical analysis, model construction, and comprehensive reasoning of educational big data are realized, and positive suggestions and countermeasures are provided for education and teaching. On the basis of the pattern analysis of educational data mining process, the concrete application of educational data mining is analyzed, and the correlation and rules between educational phenomena are found, so as to provide educational prediction and educational decision support for further optimization of teaching.

University students face challenges with time management skills and stress that affects academic performance.Furthermore, the lack of awareness and access to resources for healthy sleep patterns is also a major challenge for university students to achieve academic success and overall well-being.The purpose of this study is to perform educational big data mining that analyzes the relationship between sleep management, time management, food management,, mental / mental management and academic performance and to explore the role of friends in mediation through the large ICPSR dataset (originally >90,000 sample size, 42 datasets).The study selected, merged, cleansed, and transformed the most relevant ICPSR datasets 8, 16, and 17.Pearson's correlation and mediation analysis are used to explore the relationship between student life management and academic performance by using SPSS and the PROCESS macro.Mediation analysis revealed that hangout with friends played a significant role as a mediator in the relationship between life management and academic performance.Life management also positively impacts CGPA and significantly reduces the failure rate.The results also state that sleep and time management correlate positively with academic performance, while mental and food management correlate negatively with academic performance.Mental and physical health are important contributors to overall well-being, including academic performance.Therefore, it is important to ensure that both are managed effectively.This study provides insight into the steps involved in large educational data mining, which help the researcher make a decision for future potential data mining research using large datasets.

Educational data mining (EDM) is an up-coming interdisciplinary research field, in which data mining (DM) techniques applying in educational data. Its objective is to better understand how students gain knowledge and recognize the settings in which they learn to improve educational outcomes. Educational systems can store a huge amount of data that coming from multiple sources in different formats. Each particular educational problem requires different types of the mining techniques because traditional DM techniques cannot be applied directly to these types of data and problems. There are many general data mining tools are available but these are not designed for deal with educational data and an educator cannot use these tools without knowledge of data mining concepts. To overcome from this problem many authors provided educational data mining tools with different data mining techniques for different purposes. This paper surveys the EDM Tools from 2001 to 2016 and presents the consolidated list of tools in one place to help the EDM researchers. Then categorizes the tools based on data mining methods and explains the usage of EDM Tools.

Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.

Every year students success rate was analysed by the Educational Institutions to develop their Academic standard. To identify the success rate many kinds of techniques are used such as statistics, physical examination and currently ongoing datamining techniques. Data mining Techniques was widely used in many fields, it is also used in the Educational environment known as Educational Data Mining (EDM). Educational data mining generate prototype in solving the research problems in students data and used to locate the unseen patterns in the students detailed dateset. This paper uses the EDM to characterize the distinct factors affecting the students performance by making predictions with efficient algorithms. Educational professionals have to identify the causes for the student failure in academic performance and the students not succeed in completing their education which becomes a social problem these days. The machine learning algorithms help the researchers for evaluation of student’s learning habits, their academic performance and added enhancement if required. This paper would discuss different kinds of algorithms to analyse the economic background of the students which mainly affects the students performance. The dataset was utilized from the UCI Repository of secondary school students performance and analysed using the Weka tool for the datamining process.

Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSViewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data.

Educational Data Mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.

In the domain of adaptable educational environments, our study is dedicated to achieving three key objectives: forecasting the adaptability of student learning, predicting and evaluating student performance, and employing aspect-based sentiment analysis for nuanced insights into student feedback. Using a systematic approach, we commence with an extensive data preparation phase to ensure data quality, followed by applying efficient data balancing techniques to mitigate biases. By emphasizing higher education or educational data mining, feature extraction methods are used to uncover significant patterns in the data. The basis of our classification method is the robust WideResNeXT architecture, which has been further improved for maximum efficiency by hyperparameter tweaking using the simple Modified Jaya Optimization Method. The recommended WResNeXt-MJ model has emerged as a formidable contender, demonstrating exceptional performance measurements. The model has an average accuracy of 98%, a low log loss of 0.05%, and an extraordinary precision score of 98.4% across all datasets, demonstrating its efficacy in enhancing predictive capacity and accuracy in flexible learning environments. This work presents a comprehensive helpful approach and a contemporary model suitable for flexible learning environments. WResNeXt-MJ’s exceptional performance values underscore its capacity to enhance pupil achievement in global higher education significantly.

Analytic tools are useful for detecting patterns in education data and providing insights about student performance and learning. This study compared six supervised learning algorithms (linear regression, ridge regression, the lasso, regression trees, random forests regression, gradient boosted regression) and identified features important for predicting student performance. The dataset consisted of N=1044 observations from two secondary schools in Portugal (UCI-MLR, Cortez & Silva, 2008). Performance was assessed by final grades (range: 0-20) in two courses, mathematics and Portugese. The models were fit to training data with 27 independent variables and evaluated on a testing subset. Overall, performance was lower for students in mathematics than Portugese. The models selected a similar set of variables as important for predicting performance: mother's education level, student plans for higher education, and weekly study time were positively related to predicted performance, whereas course subject, school educational support, and romantic relationships were associated with decreased student performance. The models differed in the number, weighting, order and importance given to predictor variables. Linear regression provided a model with 13 predictors. Ridge regression shrank the coefficient estimates toward zero; the lasso performed variables selection for a model with 20 predictors. There was a tradeoff between model complexity and interpretability. The single pruned regression tree provided a simple, interpretable non-linear model with four features. Random forests regression and gradient boosting reduced overfitting, but were more difficult to interpret. Advantages and limitations of the different models are discussed. Applications for educational data mining (EDM) and learning analytics (LA) are considered.