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Journal articles on the topic 'Educational data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

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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Shrestha, Sushil, and Manish Pokharel. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (April 1, 2021): 9. http://dx.doi.org/10.11591/ijict.v10i1.pp9-18.

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<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click, and Wiki.Click was used as the independent features and Grade as the dependent feature. Five classification algorithms such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine (SVM), Random Forest, and CART decision tree were applied in the moodle data. The finding shows that SVM has the highest accuracy in comparison to other algorithms. It suggested that File.Click and System.Click was the most significant feature. This type of research helps in the early identification of students’ performance. The growing popularity of the teaching-learning process through an online learning system has attracted researchers to work in the field of Educational Data Mining (EDM). Varieties of data are generated through several online activities that can be analyzed to understand the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platforms for the delivery of the course contents and the learners who use these platforms are highly benefited from this research.</p>

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Bilal Zorić, Alisa. "Benefits of Educational Data Mining." Journal of International Business Research and Marketing 6, no. 1 (2020): 12–16. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.61.3002.

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We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.

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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 (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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Hussain, Sadiq, and Hazarika G.C. "Educational Data Mining Using JMP." International Journal of Computer Science and Information Technology 6, no. 5 (October 31, 2014): 111–20. http://dx.doi.org/10.5121/ijcsit.2014.6509.

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Bachhal, P., S. Ahuja, and S. Gargrish. "Educational Data Mining: A Review." Journal of Physics: Conference Series 1950, no. 1 (August 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/1950/1/012022.

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Mohamad, Siti Khadijah, and Zaidatun Tasir. "Educational Data Mining: A Review." Procedia - Social and Behavioral Sciences 97 (November 2013): 320–24. http://dx.doi.org/10.1016/j.sbspro.2013.10.240.

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8

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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M. D. A. Ali, Felermino, and S. C. Ng. "Moodle Data Retrieval for Educational Data Mining." International Journal of Scientific Engineering and Technology 4, no. 11 (November 1, 2015): 523–25. http://dx.doi.org/10.17950/ijset/v4s11/1105.

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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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Kaur, Balwinder, Anu Gupta, and R. K. Singla . "An Insight into Educational Data Mining." International Journal of Computer Sciences and Engineering 7, no. 2 (February 28, 2019): 83–90. http://dx.doi.org/10.26438/ijcse/v7i2.8390.

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Suthar, Ms Falguni. "A Study on Educational Data Mining." International Journal for Research in Applied Science and Engineering Technology 7, no. 2 (February 28, 2019): 1089–96. http://dx.doi.org/10.22214/ijraset.2019.2172.

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Sharma, Tanuja. "Educational Data Mining-Students Performance Prediction." International Journal for Research in Applied Science and Engineering Technology 7, no. 8 (August 31, 2019): 454–67. http://dx.doi.org/10.22214/ijraset.2019.8063.

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Manek, Saurabh, Saurav Vijay, and Deepali Kamthania. "Educational data mining - a case study." International Journal of Information and Decision Sciences 8, no. 2 (2016): 187. http://dx.doi.org/10.1504/ijids.2016.076517.

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Harikumar, Smitha. "A Study on Educational Data Mining." International Journal of Computer Trends and Technology 8, no. 2 (February 25, 2014): 90–95. http://dx.doi.org/10.14445/22312803/ijctt-v8p117.

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Kharis, Selly Anastassia Amellia, and Arman Haqqi Anna Zili. "Learning Analytics dan Educational Data Mining pada Data Pendidikan." JURNAL RISET PEMBELAJARAN MATEMATIKA SEKOLAH 6, no. 1 (March 31, 2022): 12–20. http://dx.doi.org/10.21009/jrpms.061.02.

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Salah satu kelebihan dari pemanfaatan teknologi pada masa pandemi di dunia pendidikan adalah meningkatnya digital footprint. Digital footprint dapat berupa data mengenai diskusi antara pendidik dan peserta didik, nilai diskusi, nilai tugas, nilai ujian, kehadiran, frekuensi mengakses materi, dan sebagainya. Dengan bertambahnya digital footprint, semakin banyak hal yang dapat diketahui dan digali. Penggalian data pendidikan (educational data mining) telah dilakukan beberapa negara untuk menganalisis dan menyelesaikan isu-isu dalam bidang pendidikan. Educational Data Mining telah secara luas dipergunakan untuk berbagai keperluan seperti analisa gaya belajar siswa, kajian efektivitas bahan ajar, prediksi tren kinerja peserta didik, simulasi pengambilan keputusan, dan lain-lain. Penelitian ini bertujuan untuk menjelaskan pengertian, penggunaan, dan dampak Learning Analytics dan Educational Data Mining pada data pendidikan. Penelitian ini menggunakan metode studi literatur dengan pendekatan kualitatif. Sumber-sumber pada penulisan artikel ini berasal dari jurnal dan buku. Teknik analisis yang digunakan pada penelitian ini adalah teknik analis isi. Learning Analytics dan Educational Data Mining menggabungkan beberapa ilmu seperti statistika, data mining, machine learning. Learning Analytics dan Educational Data Mining memiliki potensi untuk dikembangkan pada data pendidikan di Indonesia. Learning Analytics dan Educational Data Mining dapat memberikan informasi kepada institusi, pendidik, dan peserta didik sehingga mendukung analisis prediksi dan pada akhirnya dapat meningkatkan motivasi, kinerja dan hasil dari suatu proses pembelajaran. Dengan melacak, menggabungkan, dan menganalisis digital footprint peserta didik, jalur baru untuk kebijakan pendidikan hadir secara lebih terbuka sesuai dengan pertumbuhan database peserta didik

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Paul, Prantosh K., Kiran Lata Dangwal, and B. B. Sarangi. "Web Data Mining: Contemporary and Future Trends: Emphasizing Educational Data Mining [EDM]." Learning Community-An International Journal of Educational and Social Development 4, no. 3 (2013): 273. http://dx.doi.org/10.5958/j.2231-458x.4.3.016.

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18

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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19

Premalatha, M., V. Viswanathan, G. Suganya, M. Kaviya, and Aparna Vijaya. "Educational Data Mining and Recommender Systems Survey." International Journal of Web Portals 10, no. 1 (January 2018): 39–53. http://dx.doi.org/10.4018/ijwp.2018010104.

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Data mining techniques are widely used for various educational researches. This article depicts the survey of various data mining techniques and tools which are used to guide students, course instructors, course developers, course administrators and organizations in respective fields based on future scope. This article also highlights how recommender systems rule the educational field though it's filtering mechanisms in recommending courses for students. It also illustrates future scope of data mining in educational needs.

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20

Nahar, Khaledun, Boishakhe Islam Shova, Tahmina Ria, Humayara Binte Rashid, and A. H. M. Saiful Islam. "Mining educational data to predict students performance." Education and Information Technologies 26, no. 5 (May 25, 2021): 6051–67. http://dx.doi.org/10.1007/s10639-021-10575-3.

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21

Ugalde, Bernard, and R. Venkateswaran. "A Research Travelogue Towards Educational Data Mining." International Journal of Computer Applications 179, no. 42 (May 17, 2018): 39–48. http://dx.doi.org/10.5120/ijca2018917005.

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Upadhyay, Nitya. "Educational Data Mining by Using Neural Network." International Journal of Computer Applications Technology and Research 5, no. 2 (February 10, 2016): 104–9. http://dx.doi.org/10.7753/ijcatr0502.1013.

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23

Shirinkina, Elena Viktorovna. "Methods of data mining and educational analytics." Современное образование, no. 1 (January 2022): 51–67. http://dx.doi.org/10.25136/2409-8736.2022.1.37582.

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The relevance of the study is due to the fact that there are currently more questions than specific answers on the topic in the context of intellectual analysis of educational data: how it is done, for what and how we can use it, what metrics to include in the sample and how to make forecasts. Undoubtedly, in the coming years there will be a transition from discussions to the practical implementation of educational analytics in educational processes. The purpose of the study is to systematize the methods of intellectual analysis of educational data in the context of the difference between educational analytics and pedagogical diagnostics and other methods of data collection. The results of the study will help to build a learning strategy and combine the objectives of the training program with the effectiveness of the educational process and the expected results from the students. In this regard, the author considers the types of educational analytics. The scientific novelty of the research lies in the systematization of the areas of research interests related to data mining in education and educational analytics. It is proved that educational analytics in combination with intellectual analysis of educational data makes it possible to develop accurate models that characterize the behavior of students, their properties, weaknesses and strengths of content and interaction with it, team and group dynamics. The practical significance of the study lies in the fact that the methods considered will allow to assess the current state of the training system or program, predict the desired results and draw up a roadmap of planned changes. For pedagogical designers and methodologists, the presented methods will become the foundation for optimizing the program. Thanks to the presented methods, students receive the most relevant, engaging and meaningful educational experience.

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Triayudi, Agung, and Wahyu Oktri Widyarto. "Educational Data Mining Analysis Using Classification Techniques." Journal of Physics: Conference Series 1933, no. 1 (June 1, 2021): 012061. http://dx.doi.org/10.1088/1742-6596/1933/1/012061.

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WALLDÉN, Sari, and Erkki MÄKINEN. "Educational Data Mining and Problem-Based Learning." Informatics in Education 13, no. 1 (April 15, 2014): 141–56. http://dx.doi.org/10.15388/infedu.2014.08.

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Ranjan, Jayanthi, and Kamna Malik. "Effective educational process: a data‐mining approach." VINE 37, no. 4 (October 30, 2007): 502–15. http://dx.doi.org/10.1108/03055720710838551.

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27

Dutt, Ashish, Maizatul Akmar Ismail, and Tutut Herawan. "A Systematic Review on Educational Data Mining." IEEE Access 5 (2017): 15991–6005. http://dx.doi.org/10.1109/access.2017.2654247.

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Wook, Muslihah, Zawiyah M. Yusof, and Mohd Zakree Ahmad Nazri. "Educational data mining acceptance among undergraduate students." Education and Information Technologies 22, no. 3 (April 14, 2016): 1195–216. http://dx.doi.org/10.1007/s10639-016-9485-x.

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kash, B. R. Pra, Dr M. Hanuman thappa, and Vasantha Kavitha. "Big Data in Educational Data Mining and Learning Analytics." International Journal of Innovative Research in Computer and Communication Engineering 02, no. 12 (December 30, 2014): 7515–20. http://dx.doi.org/10.15680/ijircce.2014.0212044.

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Banerjee, Soumita. "The Role of Global Educational Database in Educational Data Mining." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 16–26. http://dx.doi.org/10.24018/ejeng.2016.1.6.194.

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Educational Data Mining is one of the major on-going research platforms now. Students’ records need to be maintained and analyzed in a manner so that they can be utilized to predict students’ behavior and learning methods. Although students’ academic records need to be processed and analyzed through data mining tools, the primary challenge is to gather individual academic student details. This paper proposes a global database of students irrespective of geographical boundaries. Academic performance of every student from every country will be updated in this platform. Students’ performance on major examinations will be available in the database. Supporting documents and performance details will be readily available and accessible to the evaluators from any geographic location. This will be helpful to standardize the evaluation process and analyze the performance of a student, irrespective of geographic boundaries. The following paper will discuss the available EDM tools and how data can be analyzed to extract information.

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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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Peña-Ayala, Alejandro. "Educational data mining: A survey and a data mining-based analysis of recent works." Expert Systems with Applications 41, no. 4 (March 2014): 1432–62. http://dx.doi.org/10.1016/j.eswa.2013.08.042.

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P. Lyras, Dimitrios, Theodor C. Panagiotakopoulos, Ilias K. Kotinas, Chris T.Panagiotakopoulos, Kyriakos N. Sgarbas, and Dimitrios K. Lymberopoulos. "Educational Software Evaluation : A Study from an Educational Data Mining Perspective." International journal of Multimedia & Its Applications 6, no. 3 (June 30, 2014): 01–20. http://dx.doi.org/10.5121/ijma.2014.6301.

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Kumar, S. Anupama. "Edifice an Educational Framework using Educational Data Mining and Visual Analytics." International Journal of Education and Management Engineering 6, no. 2 (March 8, 2016): 24–30. http://dx.doi.org/10.5815/ijeme.2016.02.03.

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Gamboa, Gregorio Z. "Clustering Scholarship Programs Using Educational Data Mining Techniques." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 3 (June 25, 2019): 658–62. http://dx.doi.org/10.30534/ijatcse/2019/51832019.

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Pena Mendes, Vitória Maria, Raniel Gomes Silva, and Alexandre Magno Andrade Maciel. "Usability Analysis of an Educational Data Mining Framework." Revista de Engenharia e Pesquisa Aplicada 6, no. 3 (April 1, 2021): 31–38. http://dx.doi.org/10.25286/repa.v6i3.1685.

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This paper presents a usability analysis of an educational data mining framework called FMDEV. The overall goal is to understand how this framework can provide a better usability to users that do not have a prior knowledge of Data science. Through a heuristic evaluation, usability problems were revealed, and usability tests confirmed these problems were affecting the user’s journey while interacting with the system. The results of this analysis indicates that it is possible to achieve an approximation between data mining tools and non-technical professionals when their behavior as real users are taken into account in the system development process.

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Bezerra, Luis Naito Mendes, and Márcia Terra Silva. "Educational Data Mining Applied to a Massive Course." International Journal of Distance Education Technologies 18, no. 4 (October 2020): 17–30. http://dx.doi.org/10.4018/ijdet.2020100102.

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

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P. Algur, Siddu, Prashant Bhat, and Nitin Kulkarni. "Educational Data Mining: Classification Techniques for Recruitment Analysis." International Journal of Modern Education and Computer Science 8, no. 2 (February 8, 2016): 59–65. http://dx.doi.org/10.5815/ijmecs.2016.02.08.

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Nuankaew, Pratya. "Self-Regulated Learning Model in Educational Data Mining." International Journal of Emerging Technologies in Learning (iJET) 17, no. 17 (September 8, 2022): 4–27. http://dx.doi.org/10.3991/ijet.v17i17.23623.

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Artificial intelligence technology brings wide impacts on several dimensions. The impact on the education system is that educational technology has been disrupted, it radically changed the paradigm of learning management. Therefore, this research aimed to study the paradigm shift of the education system focusing on the deployment of artificial intelligence technology to support the learning model in the era affected by the COVID-19 pandemic. There are two research objectives: (1) to study an appropriate self-regulated learning model with data mining techniques for designing appropriate online learning management, and (2) to study the learning achievement factors of learners by applying blended learning and self-regulated learning techniques. The samples were 26 students at the University of Phayao who enrolled in the course 221203 Technology for Business Application in the 2nd semester of the academic year 2020. The research tool is a statistical analysis and machine learning tool. It consists of analyzing pre-test scores, post-test scores, midterm scores, final scores, academic achievement, clustering analysis, and clustering performance. As a result, it found that learners had five reasonable clusters for the academic achievement learning model. The results specified the different learning styles of the learners in two dimensions including online and offline scenarios. Therefore, in future work, the researcher looks forward to performing research in the scope of identifying the suitability and the necessity of converting the face-to-face learning model to a fully online learning model.

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J, Banumathi. "Optimized Student Skill Prediction using Educational Data Mining." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 543–50. http://dx.doi.org/10.22214/ijraset.2019.3094.

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Faria de Souza, Vanessa, and Tony Carlos Bignardi dos Santos. "Research Topics on Educational Data Mining in MOOCS." International Journal for Innovation Education and Research 8, no. 7 (July 1, 2020): 311–20. http://dx.doi.org/10.31686/ijier.vol8.iss7.2481.

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Educational Data Mining Techniques have been widely used in MOOC environments to conduct different educational analyzes. In this context, a systematic mapping was conducted in five databases in order to verify which aspects of studies are inherent to the use of Educational Data Mining in MOOCs. The search comprised the period from 2015 to 2019, and 253 searches were found, out of this total, 133 studies were selected. The results revealed that studies on performance analysis, behavior analysis, forum analysis and implementation of recommendation systems are the most frequent themes.

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Karabulut, Bergen, Şeyma Cihan, Halil Murat Ünver, and Atilla Ergüzen. "ELECTROLAB: A NEW DATASET FOR EDUCATIONAL DATA MINING." e-Journal of New World Sciences Academy 13, no. 4 (October 17, 2018): 318–28. http://dx.doi.org/10.12739/nwsa.2018.13.4.2a0161.

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Wong, Jeff Chak Fu, and Tony Chun Yin Yip. "Measuring Students' Academic Performance through Educational Data Mining." International Journal of Information and Education Technology 10, no. 11 (2020): 797–804. http://dx.doi.org/10.18178/ijiet.2020.10.11.1461.

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Based on a mix of real world data and a simulated dataset for predicting the students’ academic performance, we study/compare various decision tree (DT) based algorithms (which include ID3, C4.5 and CART) with different choices of information entropy metrics (which include Shannon, Quadratic, Havrda and Charvát, Rényi, Taneja, Trigonometric and R-norm entropies) to build a decision tree in order to provide appropriate counseling/advise at an earlier stage. DT is one such important technique in educational data mining (EDM) which creates hierarchical structures of classification rules “If ⋯, Then ⋯” building a tree structure by incrementally breaking down the datasets in smaller subsets. The results suggest that basic training of the students has no significant predictive power on performance, while information about their abilities, diligence, motivation and activity in the learning process can predict their grades. As such, the resulting forecasts can be used by the instructor in optimizing the learning process and designing the course content and schedule.

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Asif, Raheela, Agathe Merceron, Syed Abbas Ali, and Najmi Ghani Haider. "Analyzing undergraduate students' performance using educational data mining." Computers & Education 113 (October 2017): 177–94. http://dx.doi.org/10.1016/j.compedu.2017.05.007.

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B., Karishma, and Swati V. "Student Performance Prediction System with Educational Data Mining." International Journal of Computer Applications 146, no. 5 (July 15, 2016): 32–35. http://dx.doi.org/10.5120/ijca2016910704.

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Pabreja, Kavita. "Comparison of Different Classification Techniques for Educational Data." International Journal of Information Systems in the Service Sector 9, no. 1 (January 2017): 54–67. http://dx.doi.org/10.4018/ijisss.2017010104.

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Data mining has been used extensively in various domains of application for prediction or classification. Data mining improves the productivity of its analysts tremendously by transforming their voluminous, unmanageable and prone to ignorable information into usable pieces of knowledge and has witnessed a great acceptance in scientific, bioinformatics and business domains. However, for education field there is still a lot to be done, especially there is plentiful research to be done as far as Indian Universities are concerned. Educational Data Mining is a promising discipline, concerned with developing techniques for exploring the unique types of educational data and using those techniques to better understand students' strengths and weaknesses. In this paper, the educational database of students undergoing higher education has been mined and various classification techniques have been compared so as to investigate the students' placement in software organizations, using real data from the students of a Delhi state university's affiliates.

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Amutha, P., and Dr R. Priya. "A survey on educational data mining techniques in predicting student’s academic performance." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 634. http://dx.doi.org/10.14419/ijet.v7i2.33.14853.

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The massive growth in the educational sector needs to create awareness about handling the huge volume of student data. The educational data mining is a technique to extract information from these volumes of data. Nowadays educational data mining technique plays a vital role in predicting academic performance. The objective of this study is to explore the extended knowledge of different educational data mining techniques, which have been used to predict the academic performance.

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Terbusheva, E. A. "Methods of teaching educational data mining for pedagogical students." Open Education 23, no. 3 (July 9, 2019): 14–24. http://dx.doi.org/10.21686/1818-4243-2019-3-14-24.

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The aim of the article is to discuss and argue teaching educational data mining for pedagogical stu-dents and to describe the methodical system of educational data mining teaching for students with a middle level of mathematical and IT disciplines, that contributes to the development of student’s research competence. The relevance of the study is determined by the requirements for the ability of higher education graduates to analyze information and perform research using modern methods and technologies that are mentioned in the educational standards and the government order. They are associated with an increasing amount of accumulated data in various fields and the cost of the knowledge extracted from data. Materials and methods. The article describes the author’s methodical system of educational data mining teaching, which was developed rely on: analysis of requirements and expectations to the re-search competence level, data analysis skills and modern education in general; comparison and analysis of the content of educational programs, books and courses on data mining and related dis-ciplines, generalization of pedagogical experience. The main aspects underlying the methodology: a form of flipped learning, a concentric (iterative) content structure, research teaching methods, a set of practical tasks for developing research competencies and Weka software for data mining as the main technical training tool for practical tasks implementation. The effectiveness of the developed methodological system was tested by the educational process monitoring, students questioning and statistical processing of questionnaires data. Results. The study shows the relevance of educational data mining teaching for students of peda-gogical universities, studying in mathematical and informational specialization. The use of the de-scribed methodic system for senior pedagogical students allows increasing the level of research competence of students and significantly developing the competence of data analysis. Conclusion. The described methodical system can used be partially or completely by teachers and methodologists for teaching data analysis at the modern level and development of research compe-tence of students with an average level of knowledge in mathematical and IT disciplines.

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Kavitha, Ganesan, and Lawrance Raj. "Educational Data Mining and Learning Analytics Educational Assistance for Teaching and Learning." International Journal of Computer & organization Trends 41, no. 1 (March 25, 2017): 21–25. http://dx.doi.org/10.14445/22492593/ijcot-v41p304.

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Mahdi, Alyaa A. "Educational Data Mining To Improve The Academic Performance in Higher Education." Cihan University-Erbil Scientific Journal 4, no. 2 (December 20, 2020): 13–18. http://dx.doi.org/10.24086/cuesj.v4n2y2020.pp13-18.

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Globalization and Innovation are mainly consider the great interest public sector and private business in the world especially in the higher education institutions. Educational Data Mining is mainly one of the business processes nowadays that attempt to bring the global innovation through improving and enhancing their processes and procedures to fulfill all the requirements and needs of the students as well as the institutions. The Educational Data Mining considered mostly concern with any research concerning the applications of the data mining and developing innovative techniques for data mining (DM) in the educational sector. This study mainly combined the use of the powerful online E-learning management system (Moodle) with data mining tools to improve the performance and effectiveness of the learning and teaching manners by using the innovative daily data that collected from the educational institutions.

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