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Journal articles on the topic 'Educative data mining'

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Author: Grafiati

Published: 25 May 2024

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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 (November 9, 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 this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.

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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 education quality increasing. Application of the corresponding instrumental means, to which the platforms of the augmented reality Vuforia, ARToolKit, Kudan can be referred, will allow presenting the lecturers the necessary tools for making of the augmented reality academic programs.

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Kumar, Raj. "Data Mining in Education: A Review." International Journal Of Mechanical Engineering And Information Technology 05, no. 01 (January 26, 2017): 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.

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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 (April 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 qualitative data is collected and analyzed, then followed by the collection and analysis of quantitative data, which aims to strengthen the results of the study. The results showed that the potentials of geo-tourism in ex-andesite stone mining area i.e. lake waters, the uniqueness of andesitic stone outcrops, and the view of landscape that overgrown by various plantation crops. Based on the results of the feasibility analyses of geo-tourism, then obtained a feasible value for the geological criteria of physical components (score = 26.334), sustainable for the economic components (score = 20.114), sustainable for the conservation components (score = 10.971), and educative (score = 8.518). Meanwhile, for the accessibility component is declared to be less feasible (score = 61.446).Keberadaan kubangan besar yang merupakan area bekas penambangan batu andesit yang tidak terkelola secara maksimal menjadi fokus penelitian ini. Penelitian ini bertujuan untuk mengkaji potensi obyek geowisata pada lahan di kawasan bekas tambang batu andesit (Setu Jayamix), serta mengetahui nilai kelayakan obyek geowisata di kawasan danau bekas tambang batu andesit tersebut (Setu Jayamix). Metode kombinasi (mix methods), yaitu gabungan antara metode kualitatif dan kuantitatif dengan model penelitian sequential exploratory design digunakan dalam penelitian ini. Sequential exploratory design merupakan model penelitian dimana data kualitatif dikumpulkan dan dianalisis, kemudian diikuti dengan pengumpulan dan analisis terhadap data kuantitatif, yang tujuannya untuk memperkuat hasil penelitian. Hasil penelitian menunjukan bahwa potensi-potensi geowisata yang terdapat di kawasan lahan bekas tambang batu andesit (Setu Jayamix) adalah perairan setu, keunikan singkapan batu andesit, serta pemandangan lanskap kawasan yang ditumbuhi berbagai tanaman perkebunan. Berdasarkan hasil analisis kelayakan geowisata, maka diperoleh nilai layak untuk kriteria geologis komponen fisik (skor = 26,334), berkelanjutan untuk komponen ekonomi (skor = 20,114), berkelanjutan untuk komponen konservasi (skor = 10,971), serta edukatif (dengan skor = 8,518). Sedangkan untuk komponen aksesibilitas dinyatakan kurang layak (skor = 61,446).

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5

Bunkar, Kamal. "Educational Data Mining in Practice Literature Review." Journal of Advanced Research in Embedded System 07, no. 01 (March 26, 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 the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.

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6

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 (April 29, 2015): 333–53. http://dx.doi.org/10.1002/wcs.1350.

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7

Romero, Cristobal, and Sebastian Ventura. "Data mining in education." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 1 (December 14, 2012): 12–27. http://dx.doi.org/10.1002/widm.1075.

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8

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 (October 5, 2023): 1287–92. http://dx.doi.org/10.21275/sr231014155301.

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9

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 информации из большого массива, требуемой для принятия решений в определенных сферах деятельности человека. На сегодняшний день области применения Data mining включают такие сферы, как бизнес, образование, сельское хозяйство, медицину и другие. В данном аспекте использование искусственного интеллекта, машинного обучения и методов визуализации данных имеют колоссальное значение для цифровой экономики РФ. This article reviews and substantiates the relevance of the application of data mining methods in education. The features and main methods used to analyze data in the study area are outlined. The described methods are the most relevant and usable in decision support systems. At the present stage of development of the information volume of data, the labor market requires new tools and methods to support large data warehouses for optimal sampling and obtaining the necessary information. Data mining is aimed at identifying and processing information from a large array required for decision making in certain areas of human activity. To date, the areas of application of Data mining include such areas as business, education, agriculture, medicine and others. In this aspect, the use of artificial intelligence, machine learning and data visualization methods are of great importance for the digital economy of the Russian Federation.

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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 (July 1, 2018): 96–102. http://dx.doi.org/10.29070/15/57537.

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11

Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (March 21, 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 machine learning. Social networking is about finding patterns in user behaviour and user engagement [5][6].

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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 (January 20, 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 other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous. Method: This study employs multiple methods for machine learning to forecast student progress. With its accurate data sample prediction, five integrated classification algorithms have been created to forecast students' academic success (support vectors, decision-making trees algorithm and perceptron algorithm, logistic regression algorithm and a random forest algorithm). Results: Students' academic achievement has been reviewed and assessed. The performance of five learning machines mentioned in Section 4 is discussed here. First, we displayed the data after pre-processing by simply displaying distributions to form the data packet and then evaluated 5 important learning methods and described the variables in the data set. The entire series of 480 characteristics were examined.

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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 (February 28, 2017): 426–32. http://dx.doi.org/10.22214/ijraset.2017.2059.

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14

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 (May 1, 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 can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

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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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16

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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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 the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

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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 (January 18, 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 (December 31, 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 (November 6, 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, followed by clustering and association. Further, this research also contains the study conducted on moodle data to find anomalies. K-means clustering was applied to find the optimal number of clusters on moodle data that consists of log and quiz dataset. The growth in the number of Internet users has increased learning through the online process. Hence, several activities are performed in OL systems, which generate a massive amount of data to be analysed to obtain useful information. Therefore, this type of research is very beneficial to academicians and instructors to identify the learner’s behaviors and develop suitable models.

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21

Wang, Shengnan. "Smart data mining algorithm for intelligent education." Journal of Intelligent & Fuzzy Systems 37, no. 1 (July 9, 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 (March 2007): 73–74. http://dx.doi.org/10.1016/j.profnurs.2007.02.003.

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23

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 (June 30, 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 different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

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Wang, Dong. "Educational data mining: Methods and applications." Applied and Computational Engineering 16, no. 1 (October 23, 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 data items. The Naive Bayes, Decision Tree, Neural Network, and K-Nearest Neighbors have commonly employed classification algorithms in educational data mining. Clustering is unsupervised learning, whose objective is to divide a collection of data objects into various groups, where samples within a cluster exhibit a high level of resemblance and those between clusters are dissimilar. In educational data mining, the K-means Clustering Algorithm, Grid-Based Clustering, and Hierarchical Clustering are common clustering techniques. Those data mining algorithms are used in education such as student behavior prediction, student bad behavior detection, and student grouping. Overall, this research demonstrates that education data mining has a significant potential to improve educational programmers and student results. To solve the legal and privacy issues associated with the collecting and use of educational data, however, more research and solutions are required.

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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 (January 20, 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 of the most relevant student attribute for prediction. Currently, to search for information in large databases, data mining techniques have become very popular and proven itis effective. Because of the performance and effectiveness of data mining techniques, it has been adopted by many areas such as telecommunication, education, sales management, banking, etc. In this paper, data mining algorithms were relied on to build e-learning classification models for a "student performance" data set, the proposed model includes 1000 instances with 35 attributes. Data mining algorithms have been implemented on the student performance data set in E-learning. Among these algorithms are the Decision Tree algorithm, Random Tree algorithm, Naive Bayes algorithm, Random Forest algorithm, REP Tree algorithm, Bagging algorithm and KNN algorithm. After comparing the results and conducting the assessment, the impact of the proposed features in e-learning on the student's performance was clarified. The final result of this study is important for providing greater insight into evaluating student performance in the COVID-19 pandemic and underscores the importance of data mining in education.

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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 (December 21, 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 investigación estudia la percepción estudiantil sobre la implementación de la educación remota de emergencia en la Carrera de Comunicación Social de la Universidad Central del Ecuador en el período académico 2020 – 2020, para lo cual se aplicó un sondeo de opinión a 832 estudiantes. El instrumento cualitativo recogió la opinión, y el corpus se proceso en una librería de Phyton. En general, se evidenció una valoración positiva frente a la nueva realidad educativa, se resaltó el uso de plataformas y redes sociales, así como innovadoras prácticas educativas en: clases magistrales, experimentación, tutorías y evaluaciones; entre los aspectos negativos, se cuestionó la escasa interacción docente/estudiante, la carencia de didáctica en entornos digitales, y la profundización de la brecha tecnológica.

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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 (August 30, 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 development of nation's education related standards. This paper focuses on capabilities of data mining in context of education, Also it compare three of the most common data mining techniques (ID3, C4.5 and REPTree).

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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 (December 28, 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 stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

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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 (June 13, 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 training.

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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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Xu, Shasha. "Effective Graph Mining for Educational Data Mining and Interest Recommendation." Applied Bionics and Biomechanics 2022 (August 12, 2022): 1–5. http://dx.doi.org/10.1155/2022/7610124.

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In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.

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Dol, Sunita M., and Dr P. M. Jawandhiya. "A Review of Data Mining in Education Sector." Journal of Engineering Education Transformations 36, S2 (January 1, 2023): 13–22. http://dx.doi.org/10.16920/jeet/2023/v36is2/23003.

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Abstract— Educational Data Mining (EDM) is one of the trending areas in which various researchers are working for the betterment of the student’s performance. Predicting the students’ performance is considered as an important task in education sector and is of paramount importance as predicting the performance accurately may lead to great future of students by analyzing data properly. This article presents the review of 32 research articles which are from ACM, IEEE, Springer and Elsevier research database. This article analyzes these research articles based on number of research articles considered from research database, publication year, performance parameters, number of performance parameteres used by research articles, Data Mining Techniques, number of algorithms used by research articles, and dataset size. It is found that classification technique is used in EDM for analyzing students’ data and in classification technique, mostly employed algorithms are Random Forest, Logistic Regression, Decision Tree, Naïve Bays, Support Vector Machine and Knearest Neighbour. Generally the performance parameters such as accuracy, precision, recall and F-measures are used to decide the performance of the classification algorithms. This review article will be helpful to those researchers who are working in the EDM for predicting students’ performance for the dataset obtained from education sector. Keywords—Data Mining, Educational Data Mining, Classification, Clustering, Association Rule

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Ataee, P. "Mining the (data) bank." IEEE Potentials 24, no. 4 (October 2005): 40–42. http://dx.doi.org/10.1109/mp.2005.1549758.

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Slater, Stefan, Srećko Joksimović, Vitomir Kovanovic, Ryan S. Baker, and Dragan Gasevic. "Tools for Educational Data Mining." Journal of Educational and Behavioral Statistics 42, no. 1 (September 24, 2016): 85–106. http://dx.doi.org/10.3102/1076998616666808.

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In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.

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Setiawan, Atje, and Rudi Rosadi. "SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (November 19, 2011): 52. http://dx.doi.org/10.22146/ijccs.5213.

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The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province. The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords— Expansion SAR, SAR-Kriging, quality education

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Saber, Nissreen El, Aya Gamal Mohamed, and Khalid A. Eldrandaly. "A New Data Fusion Framework of Business Intelligence for Mining Educational Data." Fusion: Practice and Applications 13, no. 1 (2023): 103–16. http://dx.doi.org/10.54216/fpa.130108.

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Student academic performance can be affected by social, economic, and educational factors. Many research works studied these factors applying to different levels in the educational organizations’ models. The importance spans giving professional educational advice to vulnerable students, supporting the student’s development of special education-related skills, and encouraging students to handle their education challenges. For educational organizations, dealing with pandemics and other obstacles has proven to be essential for education sustainability. One way is to be proactive and use the power of exploring and discovering educational data to predict students’ performance and attitude. Mining educational data can benefit from Business Intelligence (BI) in visualizing, organizing, and extracting insights for student’s performance. Educational Data Mining (EDM) is used in this research to predict students' performance. A novel data fusion framework is introduced for Business Intelligence using educational data mining. This study aims to show the techniques that predict students' performance and the most effective methods for each of them. The proposed framework used the advantage of business intelligence concepts and tools to highlight the metrics providing better statistical and analytical understanding.

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Amala Jayanthi M. and Elizabeth Shanthi I. "Role of Educational Data Mining in Student Learning Processes With Sentiment Analysis." International Journal of Knowledge and Systems Science 11, no. 4 (October 2020): 31–44. http://dx.doi.org/10.4018/ijkss.2020100103.

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Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics.

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Sarıtaş, Mustafa Tuncay, Caner Börekci, and Samet Demirel. "Quality Assurance in Distance Education through Data Mining." International Journal of Technology in Education and Science 6, no. 3 (August 26, 2022): 443–57. http://dx.doi.org/10.46328/ijtes.396.

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Learning Management Systems (LMS) are software applications that facilitate the management and monitoring of online teaching courses and/or training programs, workshops, webinars, forums, and other similar learning activities. The LMS provides learning and teaching benefits and possibilities for synchronous, asynchronous, and hybrid training. For instance, learning management systems (LMS) can store a wide variety of large-scale educational data. The stored data can be analyzed by employing educational data mining methods. Educational data mining (EDM) is a new discipline that deals with methods for exploring the unique and large-scale data generated by digital platforms to better understand students’ learning progress and the learning environment itself. In this study, the data stored in the LMS used by Balıkesir University during the fall semester of the 2021–2022 academic year were analyzed by using educational data mining methods in order to reveal the current status of distance education activities and make suggestions to improve the quality.

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Bhavani, Mudrakola, and Podila Mounika. "Educational Data Mining using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4940–47. http://dx.doi.org/10.22214/ijraset.2023.54210.

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Abstract: The ability to forecast students' performance is one of the most useful and important academic issues in the world today because of the development of technology. In the subject of education, data mining is incredibly useful, particularly for analysing student performance. The imbalanced datasets in this subject have made it extremely difficult to estimate students' performance, and there is no comparison of the various resampling techniques. This study compares multiple resampling procedures to manage the unbalanced information problem when projecting student performance of two distinctive datasets, including Borderline SMOTE, SMOTE-ENN, SMOTE, Random Over Sampler, SVM-SMOTE, and SMOTE-Tomek. Additionally, the dissimilarity between binary classification and multiclass, as well as the features' structures, are looked at. must be able to evaluate the effectiveness.

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L. la Red Martínez, David, and Carlos E. Podestá Gómez. "Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute." American Journal of Educational Research 2, no. 9 (August 14, 2014): 713–26. http://dx.doi.org/10.12691/education-2-9-3.

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Gul, Sumeer, Shohar Bano, and Taseen Shah. "Exploring data mining: facets and emerging trends." Digital Library Perspectives 37, no. 4 (October 20, 2021): 429–48. http://dx.doi.org/10.1108/dlp-08-2020-0078.

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Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.

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Suhirman, Suhirman, Tutut Herawan, Haruna Chiroma, and Jasni Mohamad Zain. "Data Mining for Education Decision Support: A Review." International Journal of Emerging Technologies in Learning (iJET) 9, no. 6 (December 8, 2014): 4. http://dx.doi.org/10.3991/ijet.v9i6.3950.

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R.B, Bhise. "“Importance of Data Mining in Higher Education System”." IOSR Journal of Humanities and Social Science 6, no. 6 (2013): 18–21. http://dx.doi.org/10.9790/0837-0661821.

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Seo, Jihoon, and Kilhong Joo. "Data-based education awareness analysis using text mining." Journal of The Korean Association of Artificial Intelligence Education 2, no. 2 (August 31, 2021): 13–20. http://dx.doi.org/10.52618/aied.2021.2.2.2.

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Al-Rawahnaa, Ammar. "Data mining for Education Sector, a proposed concept." Journal of Applied Data Sciences 1, no. 1 (September 1, 2020): 1–10. http://dx.doi.org/10.47738/jads.v1i1.6.

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Trang, Nguyen. "Data mining for Education Sector, a proposed concept." Journal of Applied Data Sciences 1, no. 1 (September 1, 2020): 11–19. http://dx.doi.org/10.47738/jads.v1i1.7.

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Fischer, Christian, Zachary A. Pardos, Ryan Shaun Baker, Joseph Jay Williams, Padhraic Smyth, Renzhe Yu, Stefan Slater, Rachel Baker, and Mark Warschauer. "Mining Big Data in Education: Affordances and Challenges." Review of Research in Education 44, no. 1 (March 2020): 130–60. http://dx.doi.org/10.3102/0091732x20903304.

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The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

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Qi, Zhimin. "Personalized Distance Education System Based on Data Mining." International Journal of Emerging Technologies in Learning (iJET) 13, no. 07 (June 28, 2018): 4. http://dx.doi.org/10.3991/ijet.v13i07.8810.

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To improve the poor intelligence and personalized service of the learning system in current distance education and training, a personalized learning system model based on data mining technology was proposed. Then, the method of applying the decision tree and BP neural network algorithm to de-sign the system was described in detail. Finally, the core module of the per-sonalized distance education system combined with Web was designed. The personalized function module introduced how the user operates the intelli-gent Web reasoning in the module according to the user input learning in-formation and gave the most matching learning materials. Finally, the appli-cation results showed that the system greatly improved the personalized ser-vice module.

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Weng, Cheng-Hsiung. "Mining fuzzy specific rare itemsets for education data." Knowledge-Based Systems 24, no. 5 (July 2011): 697–708. http://dx.doi.org/10.1016/j.knosys.2011.02.010.

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