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Artículos de revistas sobre el tema "Educative data mining"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 19 de julio de 2025

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1

Chen, Jianhui, and Jing Zhao. "An Educational Data Mining Model for Supervision of Network Learning Process." International Journal of Emerging Technologies in Learning (iJET) 13, no. 11 (2018): 67. http://dx.doi.org/10.3991/ijet.v13i11.9599.

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To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that

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Leo, Willyanto Santoso, and Yulia. "Predicting student performance in higher education using multi-regression models." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1354–60. https://doi.org/10.12928/TELKOMNIKA.v18i3.14802.

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Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student’ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regres

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Зелінська, Сніжана, Альберт Азарян, and Володимир Азарян. "Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment." Педагогіка вищої та середньої школи 51 (December 13, 2018): 263–75. http://dx.doi.org/10.31812/pedag.v51i0.3674.

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Zelinska S.O., Azaryan A.A. and Azaryan V.A. Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment. The augmented reality technologies allow receiving the necessary data about the environment and improvement of the information perception. Application of the augmented reality technologies in the information and educative environment of the higher education establishment will allow receiving the additional instrumental means for educat

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4

Zerlina, Dessy, Indarti Komala Dewi, and Sutanto Sutanto. "Feasibility analysis of lake ex-andesite stone mining as geo-tourism area at Tegalega Village, Cigudeg, Bogor." Indonesian Journal of Applied Environmental Studies 1, no. 1 (2020): 40–47. http://dx.doi.org/10.33751/injast.v1i1.1974.

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The existence of large wallow which is an ex-mining of andesite stone that is not manage properly became the focus of this study. The objective of this study was to analyse the potential of geo-tourism object at the land of ex-andesite stone mining (Setu Jayamix), as well as to find out the feasibility value of geo-tourism object at the lake of ex-andesite stone mining (Setu Jayamix). Mix methods, which is a combination of qualitative and quantitative methods with the research design of sequential exploratory was used in this study. Sequential exploratory design is a research model where the q

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Kumar, Raj. "Data Mining in Education: A Review." International Journal Of Mechanical Engineering And Information Technology 05, no. 01 (2017): 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.

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Kohli, Sankalp. "Data Mining and Its Applications in Higher Education." International Journal of Scientific Engineering and Research 11, no. 1 (2023): 51–60. https://doi.org/10.70729/se23125221124.

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Nupur, Bhagoriya* Priyanka Pande. "EDUCATIONAL DATA MINING IN THE FIELD OF HIGHER EDUCATION-A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 697–99. https://doi.org/10.5281/zenodo.569944.

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As we know that in today the higher education is important for every human being. The new trends and techniques are playing very crucial and important role to improve the quality of higher education. Now we need some more sophisticated and emerging trends that turns to improve the quality of higher education. Educational Data Mining is one of the emerging trends that concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.

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Егорова, Е. С., and Н. А. Попова. "Data Mining in education: predicting student performance." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 2(41) (2023): 3–4. http://dx.doi.org/10.26102/2310-6018/2023.41.2.003.

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Способность прогнозировать академические результаты учащихся имеет ценность для любого учебного заведения, стремящегося улучшить успеваемость и мотивацию студентов. Основываясь на сгенерированных прогнозах, учащимся, выявленным как подверженным риску отчисления или неуспеваемости, может быть оказана поддержка более своевременным образом. В статье рассмотрены различные классификационные модели для прогнозирования успеваемости студентов, используя данные, собранные в университетах г. Пензы. Данные включают сведения о зачислении студентов, а также данные о деятельности, полученные из университетс

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9

Khan, Nikhat. "Prediction of Student Admission using Fuzzy based Education Data Mining." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 660–67. http://dx.doi.org/10.21275/sr221014151159.

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10

Bunkar, Kamal. "Educational Data Mining in Practice Literature Review." Journal of Advanced Research in Embedded System 07, no. 01 (2020): 1–7. http://dx.doi.org/10.24321/2395.3802.202001.

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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

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11

Aye, Pwint Phyu, and Khaing Wai Khaing. "To Development Manufacturing and Education using Data Mining A Review." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2168–73. https://doi.org/10.5281/zenodo.3591179.

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the extraction of information from huge volume of data or set through the use of various data mining techniques. The data mining techniques like clustering, classification help in finding the hidden and previously unknown information from the database. In addition, data mining also important role and educational sector. Edu

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12

Surbhi, Agrawal, and K. Vishwakarma Santosh. "Predicting Student's Academic Performance using Data Mining Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 215–19. https://doi.org/10.35940/ijeat.B4521.029320.

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To meet the change in world in terms of digitalization and progress, the need and importance of education is known to everyone. The increasing awareness towards and digitization has given rise to increase in size of education field’s database. Such database contains information about students. The information includes students behavior, their family background, the facility they have, the society environment which surrounds them, their academic records etc. The increasing technology in data sciences can help utilize this huge education field database in a productive way by applying data

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13

Koedinger, Kenneth R., Sidney D'Mello, Elizabeth A. McLaughlin, Zachary A. Pardos, and Carolyn P. Rosé. "Data mining and education." Wiley Interdisciplinary Reviews: Cognitive Science 6, no. 4 (2015): 333–53. http://dx.doi.org/10.1002/wcs.1350.

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14

Romero, Cristobal, and Sebastian Ventura. "Data mining in education." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 1 (2012): 12–27. http://dx.doi.org/10.1002/widm.1075.

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Srinivasan, B.1 Abinaya V.2. "AN ANALYSIS ON PREDICTION OF SUPERIOR SCHOOLING APPRENTICE ACCOMPLISHMENT IN INSTRUCTIVE DATA MINING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 873–76. https://doi.org/10.5281/zenodo.834574.

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Educational Data Mining is used to predict the potential learning behavior of the learner. One of the biggest challenges that higher education faces today is predicting the paths of students. Predicting the performance of a student is a great concern to the higher education managements. [15] The possibility of this paper is to identify the factors influencing the performance of students in final examinations and find out a suitable data mining algorithm to predict the grade of students. [15] This work will help the educational institutions to classify the students who are at risk and to provid

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Pragati, Sharma, and Sanjiv Sharma Dr. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 166–77. https://doi.org/10.5281/zenodo.1202113.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining c

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17

Алисултанова, Э. Д., Л. К. Хаджиева, and З. А. Шудуева. "DATA MINING TECHNIQUES IN EDUCATION." Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no. 2(28) (August 26, 2022): 47–54. http://dx.doi.org/10.34708/gstou.2022.16.83.006.

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В данной статье проводится обзор и обосновывается актуальность применения методов интеллектуального анализа данных в образовании. Изложены особенности и основные методы, применяемые для анализа данных в исследуемой области. Описанные методы наиболее актуальны и употребимы в системах поддержки принятия решений. На современном этапе развития информационного объема данных рынок труда требует новых инструментов и методов для поддержки больших хранилищ данных для оптимальной выборки и получения необходимой информации. Интеллектуальный анализ данных (Data mining) направлен на выявление и обработку и

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18

Zahraa Raji Alzobaidy. "Data mining application in education." World Journal of Advanced Engineering Technology and Sciences 14, no. 2 (2025): 173–82. https://doi.org/10.30574/wjaets.2025.14.2.0070.

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In the digital era we live, the education produces huge amount of data from different resources like learning management system, electronic tests, students records. Educational Data Mining is the discovery of hidden useful knowledge and pattern in educational data concerned with studying and analyzing data from academic database, which is very large datasets to reveal main concept and relationships that improves education process and making more precise decisions. In this paper we explored common data mining techniques and their application in educational context, were automatically used to ex

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K, Shilpa, and Krishna Prasad K. "A Study on Data Mining Techniques to Improve Students Performance in Higher Education." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1287–92. http://dx.doi.org/10.21275/sr231014155301.

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20

Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in ma

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21

ÇIĞŞAR, Begüm, Semra BOĞA, and Deniz ÜNAL. "Determining Consumer Default Risk with Data Mining Techniques: An Empirical Analysis on Turkey." International Journal of Contemporary Economics and Administrative Sciences 13, no. 1 (2023): 084–100. https://doi.org/10.5281/zenodo.8332194.

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The aim of this study, which deals with consumer default risk, is to reveal the financial, socioeconomic, and demographic determinants of default risk at household level. Credit risk was investigated with various variables by applying data mining methods to the data set obtained from the Turkish Statistical Institute, Household Income and Living Conditions Survey covering the years 2016, 2017, 2018. Analyses were carried out using the WEKA data mining program. The findings of the study revealed that variables such as gender, age, marital status, education level, health status, employment statu

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Dwivedi, Shivendra, and Prabhat Pandey. "Efficient Data Mining Technique in Higher Education System: Analysis with Reference to Madhya Pradesh." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 96–102. http://dx.doi.org/10.29070/15/57537.

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Anatoli, Nachev. "Data Mining Techniques for Analysing Employment Data." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 555–66. https://doi.org/10.35940/ijeat.B3311.129219.

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This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity ana

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Vyas, Peeyush. "Data Mining in Higher Education Sector." International Journal for Research in Applied Science and Engineering Technology V, no. II (2017): 426–32. http://dx.doi.org/10.22214/ijraset.2017.2059.

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Sharma, Pragati, and Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining c

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Priyanka, Saini. "Data Mining Application in Advertisement Management of Higher Educational Institutes." Data Mining Application in Advertisement Management of Higher Educational Institutes 01, apr (2014): 01–11. https://doi.org/10.5281/zenodo.1436264.

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In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods

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Khudhur, Mokhalad Eesee, Mohammed Shihab Ahmed, and Saif Muhannad Maher. "Prediction of the Academic Achievement of Pupils Using Data Mining Techniques." Webology 19, no. 1 (2022): 185–94. http://dx.doi.org/10.14704/web/v19i1/web19014.

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Introduction: During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and othe

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Hasanov, F. "E-GOVERMANCE USING DATA WAREHOUSES AND DATA MINING." Sciences of Europe, no. 115 (April 24, 2023): 86–90. https://doi.org/10.5281/zenodo.7857957.

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Information and communication technologies play a dominant role in all spheres of government activity. If everyone interaction with the government can be carried out through a single portal that is available at any time, without standing in queues at leisure at home, then it will be very convenient for all citizens. This will help the government to avoid corruption and interact directly with people, it will additionally help state institutions to increase operational efficiency, reduce project costs and become closer to citizens in a number of areas, which include transport, municipal archives

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P.Saraswathi, *1 DR. N. Nagadeepa 2. "EXPLORING FACTORS INFLUENCE SPECIAL STUDENTS PERFORMANCE USING EXTRACTION METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 429–32. https://doi.org/10.5281/zenodo.1147612.

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Today the existing challenge in higher education is to make students proficiently. Data mining is one of the best to education rising technique for prediction.  It is the way intended to sustain the accurate necessitate of disability students in the education system. Education is the most important area undergone the influence of innovations in digital revolution. In mining, association rule is the popular approach to set up the association among items. Other than academic variables, huge number of factors plays the important role in prediction. The performance of disability student becom

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Johnson, Jeffrey Alan. "The Ethics of Big Data in Higher Education." International Review of Information Ethics 21 (July 1, 2014): 3–10. http://dx.doi.org/10.29173/irie365.

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Data mining and predictive analytics—collectively referred to as “big data”—are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of

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Y, Divyabharathi. "A Framework for Student Academic Performance Using Naive Bayes Classification Technique." J. of Advancement in Engineering and Technology 6, no. 3 (2018): 08. https://doi.org/10.5281/zenodo.1277183.

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The real fact in the education institute is the significant growth of the educational data. Data mining techniques are used to extract the useful information and to predict the student academic performance. The main aim of this paper is to construct predictive model for student academic performance. As there are many classification techniques are available, in this paper   naive bayes classification technique is used. This paper presents and analyses the experience of applying certain data mining methods and techniques on student data in order to prevent academic risk and desertion.

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AbdulazizAlHammadi, Dina, and Mehmet Sabih Aksoy. "Data Mining in Education- An Experimental Study." International Journal of Computer Applications 62, no. 15 (2013): 31–34. http://dx.doi.org/10.5120/10158-5035.

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Venkataratnam, B., G. Sravanthi, and C. Deepa. "Data Mining is used in Education System." International Journal of Computer Sciences and Engineering 6, no. 12 (2018): 810–12. http://dx.doi.org/10.26438/ijcse/v6i12.810812.

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Shrestha, Sushil, and Manish Pokharel. "Data Mining Applications Used in Education Sector." Journal of Education and Research 10, no. 2 (2020): 27–51. http://dx.doi.org/10.3126/jer.v10i2.32721.

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The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification,

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Wang, Shengnan. "Smart data mining algorithm for intelligent education." Journal of Intelligent & Fuzzy Systems 37, no. 1 (2019): 9–16. http://dx.doi.org/10.3233/jifs-179058.

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Lancaster, Jeannette. "Mining the Data on Professional Nursing Education." Journal of Professional Nursing 23, no. 2 (2007): 73–74. http://dx.doi.org/10.1016/j.profnurs.2007.02.003.

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Tarun, Ivy M., Bobby D. Gerardo, and Bartolome T. Tanguilig III. "Generating Licensure Examination Performance Models Using PART and JRip Classifiers: A Data Mining Application in Education." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 202–7. http://dx.doi.org/10.7763/ijcce.2014.v3.320.

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Du, Jiang Yi, and Yi Meng Chen. "Applicatiions and Research of Data Mining in Teaching." Applied Mechanics and Materials 58-60 (June 2011): 2659–63. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2659.

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Data mining technology has been widely used in the retail, finance, telecommunications and many other industries. With the promotion of education informationiation, useing the data mining technology in network education , finding useful knowledge in large education data to guide education and develop education become a necessary research. Based on the descriptionof the concept，characteristics, methods, and implement process of data mining, this paper introduces its several applications in teaching and the positive effect of teaching.

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Mr. Bhushan Bandre, Ms. Rashmi Khalatkar. "Impact of Data Mining Technique in Education Institutions." International Journal of New Practices in Management and Engineering 4, no. 02 (2015): 01–07. http://dx.doi.org/10.17762/ijnpme.v4i02.35.

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Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using diff

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Wang, Dong. "Educational data mining: Methods and applications." Applied and Computational Engineering 16, no. 1 (2023): 205–9. http://dx.doi.org/10.54254/2755-2721/16/20230892.

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Educational data mining is a rapidly growing field that applies various statistical and data mining techniques to analyze educational data. This paper provides a general review of the literature on educational data mining, focusing on the methods and applications. Methods used in education data mining include classification and clustering. A classification algorithm is a supervised learning technique that seeks to categorize a given set of data objects into specified categories, build a classification model using the input data that already exists, and then apply the model to categorize new da

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?alik, Krista Rizman. "Learning through data mining." Computer Applications in Engineering Education 13, no. 1 (2005): 60–65. http://dx.doi.org/10.1002/cae.20030.

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Stamper, John, Steven Moore, Carolyn Rose, Philip Pavlik, and Kenneth Koedinger. "LearnSphere: A Learning Data and Analytics Cyberinfrastructure." Journal of Educational Data Mining (JEDM) 16, no. 1 (2024): 141–63. https://doi.org/10.5281/zenodo.11109638.

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LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation ineducation. It supports learning researchers in addressing a broad range of issues including cognitive, social,and motivational factors in learning, educational content analysis, and educational technology innovation.LearnSphere integrates previously separate educational data and analytic resources developed byparticipating institutions. The web-based workflow authoring tool, Tigris, allows technical users tocontribute sophisticated analytic methods, and learning researchers can adapt and

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Grljević, Olivera, and Zita Bošnjak. "Sentiment discovery and analysis as a mean of student experience improvement." Perspectives of Innovations, Economics and Business 17, no. 1 (2018): 52–60. https://doi.org/10.15208/pieb.2017.04.

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Sentiment analysis has found broad usage helping institutions to better understand the choices, intentions, and behaviors of an individual acting as a buyer, consumer or service user. However its utilization in the domain of higher education is scarce. Therefore, the paper provides an insight into most relevant research and diversified applications of sentiment analysis in higher education, describing its unexploited potentials and benefits, such as leveraging students’ attraction/retention, evaluating the institution’s competitiveness or tracking performance indicators over time.

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Huang, Lin Na, and Guo Xiang Liu. "Application of Web Data Mining in On-Line Education." Advanced Materials Research 684 (April 2013): 526–30. http://dx.doi.org/10.4028/www.scientific.net/amr.684.526.

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On-line education, as a new teaching method, introduces Web data mining into on-line education to develop intelligentized and individual construction of resource library and on-line education. Web data mining technology can help to find out education laws and modes to meet different students’ individuation, reaching Network level teaching and improving Network teaching quality. This paper analyses problems existed in current on-line education by pointing out necessary Web data mining technology and its application in on-line education.

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Elaraby, Ibrahim Sayed. "Using Data Mining Technique to Analyze Student's Performance." INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY 5, no. 2 (2014): 586–91. http://dx.doi.org/10.24297/ijrem.v5i2.3903.

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Educational organizations are one of the important parts of our society and playing a vital role for growth and development of any nation. With the help of Data Mining, which is an emerging technique, one can efficiently learn from historical data and use that obtained knowledge for predicting future behaviour of concern areas. Growth of current education system is surely enhanced if Data Mining has been adopted as a futuristic strategic management tool. The Data Mining tool is able to facilitate better resource utilization in terms of student performance, course development and finally the de

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Sheena Angra and Sachin Ahuja. "Analysis of Student's Data using Rapid Miner." Journal on Today's Ideas - Tomorrow's Technologies 4, no. 2 (2016): 109–17. http://dx.doi.org/10.15415/jotitt.2016.42007.

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Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stake

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Nguyen, Thanh Ngoc Dan, and Vi Thi Thuy Ha. "AN OVERVIEW OF EDUCATIONAL DATA MINING." Scientific Journal of Tra Vinh University 1, no. 1 (2019): 56–60. http://dx.doi.org/10.35382/18594816.1.1.2019.88.

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Higher education data is growing, but the exploitation and extraction of meaningful knowledge for management have not been paid much attention. The existing mining tools are not effective. This study aims to introduce three techniques for educational data mining: (1) Classification techniques, (2) Predictive models, (3) Clustering techniques. Simultaneously, the study also proposes some solutions to analyze and visualize data, predict students’ learning capacity and assemble learners. Thereby, education managers could choose appropriate data mining solutions for effective management and traini

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BENAVIDES-MORALES, Ana Cristina, and Boris Enrique HERRERA-FLORES. "Educational Data Mining: Análisis de sentimientos en un dominio universitario durante la pandemia." Chasqui. Revista Latinoamericana de Comunicación 1, no. 151 (2022): 217–36. http://dx.doi.org/10.16921/chasqui.v1i151.4760.

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La nueva normalidad provocada por la emergencia sanitaria del COVID 19 obligó a la sociedad a adoptar normas de aislamiento obligatorio, las medidas modificaron dinámicas socioculturales, económicas, laborales, y sobre todo la interacción social. En el campo educativo, los establecimientos de enseñanza en general cerraron sus instalaciones y adoptaron herramientas tecnológicas para suplir el aula física, se inauguró así un período de educación remota de emergencia (Hodges et al., 2020) (Portillo, S., Castellanos, L., Reynoso, O., & Gavotto, O., 2020) (Castañeda y Vargas, 2021). Esta invest

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Alsammak, Ihab L. Hussein, Ali Hussein Mohammed, and ntedhar Shakir Nasir. "E-learning and COVID-19: Predicting Student Academic Performance Using Data Mining Algorithms." Webology 19, no. 1 (2022): 3419–32. http://dx.doi.org/10.14704/web/v19i1/web19225.

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The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification

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Mr., Abdul Samad Riyaz Ahmed Khan, and Prashant Mulay Dr. "Utilizing Data Mining Methods in the Field of Education." International Journal of Advance and Applied Research S6, no. 22 (2025): 964–66. https://doi.org/10.5281/zenodo.15534019.

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<em>Assessment technologies, learning management systems, and student information systems all provide enormous volumes of data for educational institutions. By gleaning insightful information from this data, stakeholders may monitor student performance, enhance learning outcomes, and streamline academic procedures. Finding patterns and trends that aid in decision-making has drawn attention to data mining (DM), also known as educational data mining (EDM), in the field of education. This paper offers a thorough analysis of the many data mining approaches used in education, emphasizing real-world

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