Applications Of Educational Data Mining Research Articles

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.

Design and Management of Micro-Teaching Mode of Education in Colleges Driven by Big Data

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.

Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems

In the era of big data, where the amount of information is growing exponentially, the importance of data mining has never been greater. Educational institutions today collect and store vast amounts of data, such as student enrollment and attendance records, and their exam results. With the need to sift through enormous amounts of data and present it in a way that anyone can understand, educational institutions are at the forefront of this trend, and this calls for a more sophisticated set of algorithms. Data mining in education was born as a response to this problem. Traditional data mining methods cannot be directly applied to educational problems because of the special purpose and function they serve. Defining at-risk students, identifying priority learning requirements for varied groups of students, increasing graduation rates, monitoring institutional performance efficiently, managing campus resources, and optimizing curriculum renewal are just a few of the applications of educational data mining. This paper reviews methodologies used as knowledge extractors to tackle specific education challenges from large data sets of higher education institutions to the benefit of all educational stakeholders.

Mining Sequential Learning Trajectories With Hidden Markov Models For Early Prediction of At-Risk Students in E-Learning Environments

With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the students learning behavior. This further helps them in data-driven decision making through timely intervention via early warning systems (EWS), reflecting and optimizing educational environments, and refining pedagogical designs. In this, the role of EWS is to timely identify the at-risk students. This study proposes a modeling methodology deploying interpretable Hidden Markov Model for mining of the sequential learning behavior built upon derived performance features from light-weight assessments. The public OULA dataset having diversified courses and 32 593 student records is used for validation. The results on the unseen test data achieve a classification accuracy ranging from 87.67% to 94.83% and AUC from 0.927 to 0.989, and outperforms other baseline models. For implementation of EWS, the study also predicts the optimal time-period, during the first and second quarter of the course with sufficient number of light-weight assessments in place. With the outcomes, this study tries to establish an efficient generalized modeling framework that may lead the higher educational institutes toward sustainable development.

Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison

Until now, many colleges are running to improve the quality of education to create a competitive environment. The wealth of data contained in the college can be put to good use according to the needs and processed into useful information to find out the relationship between the attributes of the data contained in it for analysis and the expected result in the form study achievements are related to study time, i.e. in adequate or late in the probable study period can be classified. Data mining, which refers to the analysis of data in the field of educational institutions, is also known as educational data mining (EDM). In the study conducted using two models of Naive Bayes Classifier i.e. Algorithms and C 4.5. The value of best accuracy in the Naive Bayes Classifier (NBC) algorithm model was 86.83% with a ratio of 80% training data, while in the model algorithm C 4.5 was 88.10% with a ratio of 90% training data. The application of EDM is expected to be maximized and developed so that it can contribute to the world of education and advance, especially in the field of data mining.

Applications of educational data mining and learning analytics on data from cybersecurity training

Applications of educational data mining and learning analytics on data from cybersecurity training

Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies’ application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students’ abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

Educational Data Mining Applied to a Massive Course

In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate computational solutions, such as educational data mining (EDM). Previous studies have tested the application of EDM on small datasets. The main contribution of the present study is the application of EDM algorithms and the analysis of the results in a massive course delivered by a Brazilian University to 181,677 undergraduate students enrolled in different fields. The use of key algorithms in educational contexts, such as decision trees and clustering, can reveal relevant knowledge, including the attribute type that most significantly contributes to passing a course and the behavior patterns of groups of students who fail.

RETRACTED ARTICLE: Predictive analysis of student academic performance and employability chances using HLVQ algorithm

Educational Data Mining (EDM) uses various data mining tools and methods for different applications in the field of education. EDM applications and techniques follow both pure and practical research objectives to enhance the learning process and to improve and develop learning quality. Educational data mining helps in forecasting the future patterns to make the organizations or institutions provide quality based education to the learners. Educational institutions still struggle with graduation and employment toll. It is an appropriate demonstration of a “surplus of information but a starvation of knowledge”. It is essential to identify the prospective ability of student by predicting the present performance by means of earlier period performance and awareness to ensure the student start the career and move ahead in the right path for better quality. This paper presents a novel hybrid algorithm, HLVQ to predict student academic performance and employability chances.

A Comparison of EDM Tools and Techniques

Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. Such a practice ensures not only better academic outcomes but also helps the institutions to reorient their curriculums and teaching pedagogies so as to add to the students’ learning curve. Educational Data Mining (EDM) has risen as a useful technology in this league. EDM techniques are now being used for predicting the enrolment of students in a specific course, detection of any irregular grades, prediction about students’ performance, analyzing and visualizing of data, and providing feedback for overall improvement in the academic spheres. This paper reviews the studies related to EDM, including the approaches, data sets, tools, and techniques that have been used in those studies, and points out the most efficient techniques. This review paper uses true prediction accuracy as a standard for the comparison of different techniques for each EDM applications of the surveyed literature. The results show that the J48 and K-means are the most effective techniques for predicting the students’ performance. Furthermore, the results also cite that Bayesian and Decision Tree Classifiers are the most widely used techniques for predicting the students’ performance. In addition, this paper highlights that the most widely used tool was WEKA, with approximately 75% frequency. The present study’s empirical assessments would be a significant contribution in the domain of EDM. The comparison of different tools and techniques presented in this study are corroborative and conclusive, thus the results will prove to be an effective reference point for the practitioners in this field. As a much needed technological asset in the present day educational context, the study also suggests that additional surveys are recommended to be driven for each of the EDM applications by taking into account more standards to set the best techniques more accurately.

Educational Data Mining Applications and Techniques

Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educa-tional environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students’ performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

Educational Data Mining: Enhancement of Student Performance model using Ensemble Methods

Nowadays, Educational Data Mining (EDM), begun as a new research area due to the broadening of numerous statistical approaches that are used to perform data exploration in educational settings. One of the applications of EDM is the prediction of performance of students. In a web based education system, the behavioural features of learners are very significant in showing the interaction between students and the LMS. In this paper, our aim is to propose a new performance prediction model for students which is based on data mining methods which includes new features known as behavioural features of students and based on sequential feature selection which is used to identify most important features. The proposed performance model is evaluated using classifiers such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Decision Tree (DT). Furthermore, so as to enhance the classifiers performance, the ensemble methods such as Bagging, Boosting and Random Forest were applied. The achieved results show that there exists a strong relationship between behaviour of students and their academic performance. An accuracy of 91.5% was gotten when the ensemble methods were applied to the classifiers to improve the academic performance. Thus, the result gotten shows the reliability of the proposed model.

Application of EDM to Understand the Online Students' Behavioral Pattern

In distance learning, the professor cannot see that the students are having trouble with a subject, and can fail to perceive the problem in time to intervene. However, in learning management systems (LMS's) a large volume of data regarding online access, participation and progress can be registered and collected allowing analysis based on students' behavioral patterns. As traditional methods have a limited capacity to extract knowledge from big volumes of data, educational data mining (EDM) arises as a tool to help teachers interpreting the behavior of students. The objective of the present article is to describe the application of educational data mining technics aiming to obtain relevant knowledge of students' behavioral patterns in an LMS for an online course, with 1,113 students enrolled. This paper applies two algorithms on educational context, decision tree and clustering, unveiling unknown relevant aspects to professors and managers, such as the most important examinations that contribute to students' approval as well as the most significant attributes to their success.

Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study

The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students.

Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswa dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar

Until now many universities that helped the government in accelerating improving the quality of education so the creation of a competitive environment.. The abundance of data contained in the College can be put to good use in accordance with the needs and processed into useful information so as to find out the relationship between the attributes of the data in it can be on the analysis and expected output in the form of student performance has related to the period of study i.e. can be categorized into appropriate or too late in the anticipated period of study. Data mining can be used for educational institutions or institutions and often called as Educational Data Mining (EDM). In the study carried out using two models of Naive Bayes Classifier i.e. algorithms and C 4.5. as for the value of the best accuracy in the Naive Bayes Classifier algorithm model (NBC) was 86.83% with ratio 80% training data, whereas in model algorithm C 4.5 was 88.10% with 90% training data ratio. Application of EDM and expected to be maximized and developed so that it can contribute to and progress in the world of education especially in data mining.

Toward Better Outcomes in Audiology Distance Education: An Educational Data Mining Approach.

This article introduces concepts and a general taxonomy used by the educational data mining (EDM) community, as well as examples of their applications, with the aims of providing audiology educators with a referential basis for developing this area. A narrative review was carried out to present an overview of EDM and its main methods. Some of these methods were exemplified with analysis of real data from an Internet-based specialization course on pediatric auditory rehabilitation. The review introduced EDM main concepts and applications and described methods from its area. Real data examples illustrated EDM use to predict interpersonal help-seeking, model interpersonal interaction, analyze students' trajectories within a course's module, and understand how students approached group assignments. Some of the insights provided by EDM to support teaching and learning processes were also described. EDM methods offer new tools to discover knowledge from digital traces (i.e., logs) and support key stakeholders (students, instructors, or course administrators) to raise awareness about course dynamics. This approach has the potential to foster a better understanding and management of educational processes in audiology distance education.

Educational data mining applications and tasks: A survey of the last 10 years

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

Predicting Critical Courses Affecting Students Performance: A Case Study

Predicting student academic performance is one of the important applications of educational data mining. It allows academic institutions to provide appropriate support for students facing difficulties. Classification is a data mining technique that can be used to build prediction models. In this paper, we use the ID3 decision tree induction algorithm to build prediction models for academic performance. Our models are built based on records for female students in the Bachelors program at the Information Technology (IT) department, King Saud University, Riyadh, Saudi Arabia. The results indicate that reliable predictions can be achieved based on the performance of students in second year courses. We also identify key courses that can be used as performance predictors. We believe our findings are useful for decision makers at the IT department.