Educational Data Mining Techniques Research Articles

An Analysis of Student Satisfaction and its Relationship with Academic and Social Factors in University Dropout

Over the last 50 years, Higher Education has experienced significant development, which has been associated with challenges that have yet to be solved. In this sense, university dropout is a global phenomenon that worries universities all over the world. Many research studies describe the possible causes of this phenomenon, but the most interesting are those that, due to their predictive potential, have the possibility of preventing it. The aim of this article is to study how important students perceive the different reasons for dropout in relation to the idea of abandoning the degree. For this purpose, the Spanish version of the Questionnaire of Motives for Higher Education Dropout (QMA_es; Almeida et al., 2019) was applied to a sample of 927 first- and second-year university students. These data have been processed using educational data mining (EDM) techniques, obtaining a decision tree that correctly classifies the sample in 81.1%. The results show that variables related to student satisfaction (satisfaction with academic choice, satisfaction with performance, and satisfaction with the financial situation) and to the academic and social environments play a predominant role in the approach to dropout, above other institutional, financial, and wellbeing variables.

Predicting Student Performance Using Educational Data Mining: A Review

Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified

Sustainability of Quality Management by Implementing Data Mining to Predict Academic Achievement

This research aims to predict the academic achievement of Pamulang University students using Educational Data Mining (EDM) techniques. With the increasing number of students and the complexity of academic data, it is important to apply methods that can analyze and predict learning outcomes to improve learning strategies and academic support. This study collected data from various sources, including course grades, attendance, and participation in extracurricular activities. The collected data is then analyzed using EDM techniques such as decision trees, neural networks, and support vector machines to identify patterns and factors that contribute to student academic achievement. The results of the analysis show that factors such as attendance, involvement in campus activities, and previous test scores have a significant influence on academic achievement. This research provides valuable insights for the development of targeted interventions, with the aim of improving academic outcomes and facilitating more effective learning strategies at Pamulang University. These findings also offer contributions to further research in the field of EDM and its application in higher education contexts.

Employing educational data mining techniques to predict programming students at-risk of dropping out

This research primary aimed at evaluating various predictive models in predicting programming students at risk of dropping out. It also aimed at identifying attributes that are significant in predicting students at risk of dropping. The educational data mining process (EDM) was utilized as the research framework. The study conducted a ten-fold cross-validation, revealing that the k-nearest neighbors (kNN) algorithm achieved the highest classification accuracy at 95.5%. The decision tree model followed closely with a 94.9% accuracy, logistic regression exhibited 94.4%, and the neural network model yielded a classification accuracy of 93.2%. Further analysis, including confusion matrices and receiver operating characteristic (ROC) curves, provided detailed insights into the models' performance. Notably, the decision tree algorithm excelled in identifying students who did not drop out, with a misclassification rate of 9 out of 30 for dropped students. Analysis also showed that students’ assignments completed (AC), laboratory work (LW), and attendance (ATT) were the strongest predictors in identifying students at risk of dropping. Results of the study can be used by instructors to identify in advance student at risk of dropping and provide them with the necessary intervention to improve performance in programming.

A critical review of data mining in education on the levels and aspects of education

Purpose This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education. Design/methodology/approach This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education. Findings EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention. Research limitations/implications This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered. Practical implications As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered. Social implications Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner. Originality/value This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

Using machine learning to predict student science achievement based on science curriculum type in TIMSS 2019

ABSTRACT Most educational systems use either an integrated or a separated science curriculum. However, it is unclear which of these science curricula benefits students more author and existing research provides insufficient information about the implementation details of the curriculum employed. Therefore, this study compares the effects of two science curricula on students’ science literacy, drawing on socio-ecological theory and employing educational data mining techniques. Results from Grade 8 Science students in 44 countries sampled in the Trends in International Mathematics and Science Study (TIMSS) 2019 showed that (1) the integrated curricula benefitted students marginally more than the separated curricula; (2) curriculum type was not essential in directly predicting students’ academic performance; and (3) random forest outperformed linear regression, lasso regression, decision trees, and neural networks in predicting student science achievement. This study advances our understanding of the predictors of student science performance, demonstrates that machine learning techniques can be applied successfully to examine curriculum effects, and provides directions for implementing integrated science curricula.

Review On Educational Academic Performance Analysis and Dropout Visualization by Analyzing Grades of Student

Education is a crucial aspect of a nation's development, and ensuring the success and retention of students is of paramount importance. The current studies show the need for an effective and efficient education prediction system. Education is a pivotal aspect of a country's development. It acts as a powerful tool to change the world. Education is the key to a literate society. In India, it is necessary to have an integrated web platform to analyze the academic performance and dropout rates across school, higher, and technical education. Student dropout is a significant problem for any nation. Discontinuing schooling due to financial, practical, and social reasons, as well as disappointment in examination results, is what is commonly referred to as student dropout. Educational Data Mining (EDM) techniques can help discover insights from data in educational environments, allowing tutors and researchers to predict future trends and student behavior. The use of machine learning and data mining techniques provides valuable tools for understanding the student learning environment. This literature review aims to synthesize the existing research findings on this topic and identify knowledge gaps for future research.

Assessing Students Performance Using Neutrosophic Tool

Recently, a lot of educational as well as academic researchers have shown their ultimate interest in educational data mining techniques. Thus, several studies that can definitely contribute to the improvement of the educational process are conducted. The results of this lead to several issues that have to be addressed if the students general academic performances are to be improved. In this paper, we investigate the academic performance of a set of students in their core subjects. This we did by using the concept of the neutrosophic frequency, as well as the neutrosophic relative frequency distribution for the class of scores for the set of students. The results give some form of expectation for their general performance. This case serves as a means to enhance the educational process of the chosen set of students by identifying the students at risk of failure or dropping out and predicting the students' academic level at an early stage to provide the necessary support for the at-risk students.

Investigation and the development of learning analytics dashboard in open and distance learning using big data mining

<p class="Abstract">The main aim of this study is to provide universities with a way of examining and predicting student performance. The fundamental aim and purpose of this study is to help academic institutions to analyse and predict student performance. The credibility and accuracy of the model was examined by comparing the predicted results of the model with the observed values. And educational data mining techniques were used to create student profiles. Weighted gain, classification analysis, decision tree and rule induction were used in this study. The results of the study showed that the level of students' academic performance varied according to criteria such as academic structure, faculty, mode of enrolment and gender. In order to determine the relative importance of variables, the information weight gain technique was used after generating rule induction parameters and hidden rules between data. Using data mining techniques, we can obtain both guidelines to instruct students and information to help us identify them.</p>

Hyperscale data analysis oriented optimization mechanisms for higher education management systems platforms with evolutionary intelligence

Higher education institutions face vast amounts of complex data from various sources. Extracting meaningful insights from this data can improve student outcomes and institutional performance. This paper proposes novel evolutionary optimization mechanisms for higher education management systems to enable hyperscale data analysis. An integrated framework is developed, combining educational data mining, learning analytics, and evolutionary computation techniques. The methodology employs a multi-objective evolutionary algorithm with dynamic resource allocation to optimize multiple objectives simultaneously. Adaptive learning control is incorporated to balance exploration and exploitation. Theoretical analyses provide convergence proofs for the proposed algorithms. Comprehensive experiments on real-world and synthetic datasets demonstrate the effectiveness of the proposed mechanisms compared to state-of-the-art approaches. The results show significant performance gains regarding solution quality, scalability, and computational efficiency. The proposed techniques can be a foundation for developing the next generation of intelligent higher education management systems.

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

BackgroundDropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history.MethodsData from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis.ResultsANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable.ConclusionsBoth ANN and Naïve Bayes methods can be useful for predicting medical students’ academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

Students’ Academic Performance Prediction Using Educational Data Mining and Machine Learning: A Systematic Review

Forecasting the performance of students holds paramount importance in the context of higher education since the criteria for a high quality university is based on its excellent record of academic achievements. At the present time, predicting students’ performance becomes more challenging due to the huge increase in the amount of educational data that is now available in educational databases. With the introduction of information systems and data mining and machine learning techniques in education a new era has been started to reveal the methodologies in studying and analyzing students’ academic performance and to enable the recording and retention of large volumes of data in educational institutions. This paper seeks to systematically review the current research on predicting student performance through the use of educational data mining and machine learning techniques. The review synthesizes a wide range of studies, encompassing diverse educational levels, data sources, and predictive models. A comprehensive review was conducted for available research spanning from 2015 to 2023, to provide a foundational understanding of the intelligent methods employed in forecasting student performance. The search encompassed different electronic bibliographic databases, such as IEEE Xplore, Google Scholar, and Science Direct. In this paper, 17 survey papers and 74 research papers have been examined and analyzed, emphasizing seven key aspects that aim to have interpretable models for forecasting student performance.

Application of Sustainable Education in Chinese Language Education in the Context of Big Data

Abstract This paper explores all student related data using educational data mining techniques to draw conclusions about their performance and behavior. The main idea and theoretical basis of Random Forest is described, the importance of each feature is calculated using Random Forest based Important Feature Selection Algorithm, and each feature is ranked and the best feature is selected as the effective feature for constructing the performance prediction model. By Light GBM is to further improve the GBDT algorithm and XGBoost algorithm to construct the Light GBM grade prediction model in order to improve the training speed and the prediction ability of the model. In order to verify the feasibility of the constructed model, the application of sustainable education in Chinese language education is tested from various aspects such as model testing as well as learning behavior. The results show that the accuracy of the Ligth GBM grade prediction model increases from 0.68 to 0.918 when the course progress is raised from 10 to 100, i.e., the accuracy of the Ligth GBM grade prediction model in predicting students’ grades gradually increases as the course progresses, so that it can effectively analyze the application of sustainable education in Chinese education.

Educational Computational Chemistry for In-Service Chemistry Teachers: A Data Mining Approach to E-Learning Environment Redesign

The use of technology in education has experienced significant growth in recent years. In this regard, computational chemistry is considered a dynamic element due to the constant advances in computational methods in chemistry, making it an emerging technology with high potential for application in teaching chemistry. This article investigates the characteristics and perceptions of in-service chemistry teachers who participated in an e-learning educational computational chemistry course. Additionally, it examines how educational data mining techniques can contribute to optimising and developing e-learning environments. The results indicate that teachers view incorporating computational chemistry elements in their classes positively but that this is not profoundly reflected in their teaching activity planning. On the other hand, generated statistical models demonstrate that the most relevant variables to consider in the instructional design of an e-learning educational computational chemistry course are related to participation in various course instances and partial evaluations. In this sense, the need to provide additional support to students during online learning is highlighted, especially during critical moments such as evaluations. In conclusion, this study offers valuable information on the characteristics and perceptions of in-service chemistry teachers and demonstrates that educational data mining techniques can help improve e-learning environments.

Educational Data Mining to Predict Bachelors Students’ Success

Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PDF

Um modelo de Sistema de Recomendação integrado a Metodologias Ativas, MDE e Learning Analytics para a mitigação de evasão em EaD

Distance Education enabled educational practices based on digital platforms. Despite its wide adoption, the high dropout rates are a reason for concern for teachers and institutional managers. There are initiatives to mitigate this situation, such as Educational Data Mining (EDM), Learning Analytics (LA), and the use of Recommendation Systems (RS). Although effective in specific aspects, these techniques lack mechanisms for students' motivation and pedagogical intervention by teachers, as they do not present methodological proposals to encourage learning. Therefore, this article describes an RS model that shows a differential integration of the pedagogical approach of Active Methodologies with the support of Educational Data Mining and Learning Analytics techniques to identify students with dropout risks and enhance permanence. For this, a prototype was implemented, and a case study was carried out with professors from two universities to assess functionality and acceptance. According to the TAM Model, more than 87% of teachers agree with the ease of use, and 77% agree that RS can be helpful in students' teaching and learning process. Therefore, the model contributes to teaching practices, encourages collaborative learning, and favors monitoring this process and the activities developed by the students.

TRENDS AND APPLICATIONS OF BIG DATA IN EDUCATION

Big data technologies have facilitated innovation across various industries, including healthcare, technology, and education. The utilization of this innovation is now required within the education sector at all levels. It is a progressing framework named as "Education 4.0" to accommodate the numerous needs of this sector. Specifically, this article first describes the architecture of big data and its properties compatible to Education sector . The process of utilizing large educational data and data mining techniques in education are explained. To find solutions to diverse educational problems, an ever-increasing variety of mining tools have been assembled. This study also provides insights into these techniques and other data sources for gathering educational data. Several applications have been developed to promote the use of big data techniques in higher education and highlight its benefits for the sector, but there are still a number of issues that need to be considered. We will highlight the obstacles of using big data techniques in higher education

Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques

<p style="text-align:justify">The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.</p>

Leveraging Causal Reasoning in Educational Data Mining: An Analysis of Brazilian Secondary Education

This study presents an approach to investigating the main interventions related to gains on performance using a combination of educational data mining (EDM) techniques and traditional theory-driven models. The goal is to overcome the limitation of previous EDM studies that lack of causal reasoning, which is a critical concern for educational specialists. We use large-scale assessment data from Brazil and map the main sources of unobserved confounders using causal graphs. We then use a two-way logistic regression fixed effects to account for these confounding factors. The model is evaluated for its predictive ability and further investigated through classification rules and decision trees, resulting in the proposition of new insights into the data. The findings of the study underline the importance of socio-economic factors and showcase the significant impact of faculty education policies as well as the vital role of Brazilian states in these policies.

Enhancement of E-Learning Student’s Performance Based on Ensemble Techniques

Educational institutions have dramatically increased in recent years, producing many graduates and postgraduates each year. One of the critical concerns of decision-makers is student performance. Educational data mining techniques are beneficial to explore uncovered data in data itself, creating a pattern to analyze student performance. In this study, we investigate the student E-learning data that has increased significantly in the era of COVID-19. Thus, this study aims to analyze and predict student performance using information gathered from online systems. Evaluating the student E-learning data through the data mining model proposed in this study will help the decision-makers make informed and suitable decisions for their institution. The proposed model includes three traditional data mining methods, decision tree, Naive Bays, and random forest, which are further enhanced by the use of three ensemble techniques: bagging, boosting, and voting. The results demonstrated that the proposed model improved the accuracy from 0.75 to 0.77 when we used the DT method with boosting. Furthermore, the precision and recall results both improved from 0.76 to 0.78.