Bibliografías temáticas / Educative data mining

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Literatura académica sobre el tema "Educative data mining"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Educative data mining"

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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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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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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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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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Tesis sobre el tema "Educative data mining"

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Войцун, О. Є. "Перспективи educational data mining в Україні". Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47901.

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Актуальність дослідження полягає в тому, що сучасний стан освіти вимагає використання сучасних методів для імплементації вказаних вище потреб, і educational data mining (EDM) надає унікальні можливості для дослідників і практиків. Метою роботи було виявлення перективних напрямків ЕDM для України.
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Manspeaker, Rachel Bechtel. "Using data mining to differentiate instruction in college algebra." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8542.

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Doctor of Philosophy<br>Department of Mathematics<br>Andrew G. Bennett<br>The main objective of the study is to identify the general characteristics of groups within a typical Studio College Algebra class and then adapt aspects of the course to best suit their needs. In a College Algebra class of 1,200 students, like those at most state funded universities, the greatest obstacle to providing personalized, effective education is the anonymity of the students. Data mining provides a method for describing students by making sense of the large amounts of information they generate. Instructors
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Alsuwaiket, Mohammed. "Measuring academic performance of students in Higher Education using data mining techniques." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/34680.

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Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain
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Burley, Keith Martin. "Data mining techniques in higher education research : the example of student retention." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/19412/.

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Data Mining has been used for more than a decade in a variety of differing environments. It takes an inductive approach to data analysis in that it is concerned with the extraction of patterns from the data often without preconceived ideas. Data mining is part of the field of Business Intelligence, a subject area that the author is familiar with and has taught for many years. He believes that the application of data mining techniques has much to offer within the context of higher education. However, there is little evidence that these well established techniques have previously been applied to
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Кузіков, Борис Олегович, Борис Олегович Кузиков та Borys Olehovych Kuzikov. "Сучасний стан та напрями розвитку Education Data Mining в Сумському державному університеті". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/37991.

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Однією з переваг автоматизації бізнес процесів є можливість їх глибокого аналізу з метою пошуку прихованих зв’язків між параметрами моделі і ефективністю процесів. Дослідження у цьому напрямку не пройшли осторонь систем дистанційного навчання, особливо з появою Massive Open Online Courses (MOOC). На ряду із терміном Data Mining – видобуток знань з масивів інформації, розглядають поняття «Education Data Mining» (EDM).
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Pepe, Julie. "STUDENT PERCEPTION OF GENERAL EDUCATION PROGRAM COURSES." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3545.

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The purposes of this study were to: (a) determine, for General Education Program (GEP) courses, what individual items on the student form are predictive of the overall instructor rating value; (b) investigate the relationship of instructional mode, class size, GEP foundational area, and GEP theme with the overall instructor rating value; (c) examine what teacher/course qualities are related to a high (Excellent) overall evaluation or a low (Poor) overall evaluation value. The data set used for analysis contained sixteen student response scores (Q1-Q16), response number, class size, term, found
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Allègre, Olivier. "Adapting the Prerequisite Structure to the Learner in Student Modeling." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS116.

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Les modèles d'apprenant basés sur les données visent à représenter et comprendre les connaissances des élèves ainsi que leurs autres caractéristiques métacognitives pour soutenir leur apprentissage en faisant des prédictions sur leurs performances futures. La modélisation de l'apprenant peut être abordée en utilisant différentes modèles de système complexes, chacun offrant une perspective différente sur l'étudiant et le processus d'apprentissage. Les techniques d'apprentissage automatique améliorées par la représentation de la connaissance, telles que les réseaux bayésiens, sont particulièreme
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Franco, Gaviria María Auxiliadora. "Principled design of evolutionary learning sytems for large scale data mining." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/14299/.

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Currently, the data mining and machine learning fields are facing new challenges because of the amount of information that is collected and needs processing. Many sophisticated learning approaches cannot simply cope with large and complex domains, because of the unmanageable execution times or the loss of prediction and generality capacities that occurs when the domains become more complex. Therefore, to cope with the volumes of information of the current realworld problems there is a need to push forward the boundaries of sophisticated data mining techniques. This thesis is focused on improvi
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Xu, Yonghong. "Using data mining in educational research: A comparison of Bayesian network with multiple regression in prediction." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280504.

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Advances in technology have altered data collection and popularized large databases in areas including education. To turn the collected data into knowledge, effective analysis tools are required. Traditional statistical approaches have shown some limitations when analyzing large-scale data, especially sets with a large number of variables. This dissertation introduces to educational researchers a new data analysis approach called data mining, an analytic process at the intersection of statistics, databases, machine learning/artificial intelligence (AI), and computer science, that is designed t
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Петренко, А. М. "Застосування методів EDM для розробки системи підтримки рішень". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/82374.

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Дана робота присвячена проектуванню моделі, яка б допомогла адміністратору електронної системи прийняти рішення щодо продуктивності сервера та прогнозувала б максимальну кількість унікальних користувачів та запитів при якій сервер буде неспроможний обробляти запити клієнтів. У ході роботи проведено аналіз вхідних даних, літератури, обрані методи, алгоритм виконання поставленої задачі, реалізовані наглядні графіки, що показують масштабованість електронного ресурсу, розроблено модель для допомоги адміністратору ресурса прийняти рішення щодо його продуктивності. Об’єкт дослідження — Дистанційни
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Libros sobre el tema "Educative data mining"

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Khan, Badrul H., Joseph Rene Corbeil, and Maria Elena Corbeil, eds. Responsible Analytics and Data Mining in Education. Routledge, 2018. http://dx.doi.org/10.4324/9780203728703.

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Cho, Ir-hyŏn. Haksŭp punsŏkhak ŭi ihae: Learning analytics. Pagyŏng story, 2019.

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Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.

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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a deta
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service), SpringerLink (Online, ed. Modern Issues and Methods in Biostatistics. Springer Science+Business Media, LLC, 2011.

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Maine. Bureau of Employment Security., ed. Maine occupational staffing for selected nonmanufacturing industries: Mining, construction, finance, insurance, and real estate services, except hospital and education : data for second quarter 1990. The Division, 1992.

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United States. Congress. House. Committee on Homeland Security. Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies. How data mining threatens student privacy: Joint hearing before the Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies of the Committee on Homeland Security, House of Representatives and the Subcommittee on Early Childhood, Elementary, and Secondary Education of the Committee on Education and the Workforce, House of Representatives, One Hundred Thirteenth Congress, second session, June 25, 2014. U.S. Government Printing Office, 2015.

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Holzinger, Andreas. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data: Third International Workshop, HCI-KDD 2013, Held at SouthCHI 2013, Maribor, Slovenia, July 1-3, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Kolo, Ibrahim A. Educational restoration in the Niger State College of Education: September 2001-2003 : being a speech at the 19th to 23rd convocation ceremony of the Niger State College of Education, Minna : date, Saturday, March 22nd 2003 : venue, college convocation ground. Niger State College of Education, 2003.

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Pandey, 1st Manish, and 2nd Rakesh Kumar. Data Warehousing and Mining. Selfypage Developers Pvt Ltd, 2022.

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Application Research of Data Mining Technology. LONGMAN PRESS LTD, 2023.

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Capítulos de libros sobre el tema "Educative data mining"

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Guruler, Huseyin, and Ayhan Istanbullu. "Modeling Student Performance in Higher Education Using Data Mining." In Educational Data Mining. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_4.

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Agrawal, Rakesh. "Enriching Education through Data Mining." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23780-5_1.

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Schönbrunn, Karoline, and Andreas Hilbert. "Data Mining in Higher Education." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_56.

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Agrawal, Rakesh, Sreenivas Gollapudi, Anitha Kannan, and Krishnaram Kenthapadi. "Enriching Education through Data Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21786-9_1.

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Peña-Ayala, Alejandro, and Leonor Cárdenas. "How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study." In Educational Data Mining. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_3.

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Contreras Bravo, Leonardo Emiro, Giovanny Mauricio Tarazona Bermudez, and José Ignacio Rodríguez Molano. "Big Data: An Exploration Toward the Improve of the Academic Performance in Higher Education." In Data Mining and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_59.

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Eubanks, David, William Evers, and Nancy Smith. "FINDING PREDICTORS IN HIGHER EDUCATION." In Data Mining and Learning Analytics. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch3.

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Ogrezeanu, Andreea-Elena. "Data Mining in Smart Agriculture." In Education, Research and Business Technologies. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8866-9_21.

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Agarwal, Sonali, Murli Dhar Tiwari, and Iti Tiwari. "Government Data Mining Case Studies on Education and Health." In E Governance Data Center, Data Warehousing and Data Mining. River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.

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Osorio-Acosta, Estefania. "Data Mining for Educational Management." In Encyclopedia of Education and Information Technologies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10576-1_124.

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Actas de conferencias sobre el tema "Educative data mining"

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Raut, Amey, and Shital Hajare. "Transforming Education with Data Mining: Opportunities, Applications, and Challenges." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11005098.

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Wang, Yun. "University Moral Education Management System using Ensemble Learning in Data Mining." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690914.

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"Empowering Open Education Methodologies with AI-based Strategies for the Customization of Education." In Data Mining and Data Warehauses – Sikdd 2024. Jožef Stefan Instutute, 2024. http://dx.doi.org/10.70314/is.2024.sikdd.16.

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Ponelis, Shana. "Finding Diamonds in Data: Reflections on Teaching Data Mining from the Coal Face." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3313.

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Making sense of the exponentially expanding sources of structured electronic data collected by organizations is increasingly difficult. Data mining is the extraction of implicit, previously unknown, and potentially useful information from large volumes of such data to support decisionmaking in organizations and has led to an increase in demand for students who have an understanding of data mining techniques and can apply them to organizations’ data. Thus data mining is an increasingly important component of the Information Systems curriculum in order to meet this skills demand. This paper desc
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Nduwimfura, Philbert, Yassein Nkoma, and Zheng JianGuo. "Improving education through data mining." In 2013 International Conference on Information and Communication Technology for Education. WIT Press, 2014. http://dx.doi.org/10.2495/icte130301.

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Ying Wah, Teh, and Zaitun Abu Bakar. "Investigating the Status of Data Mining in Practice." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2719.

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This paper is based on a survey carried out in the Malaysian environment. The paper starts with a definition of data warehouses, data mining and this is followed by a description of its current status in the Malaysian data mining environment. This is followed by a discussion on why data mining is a great challenge for an implementation in the Malaysian environment. Based on the feedback obtained from the respondents, a conclusion is drawn on the appropriateness of the data mining techniques in the Malaysian environment
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Wang, Xiaodan. "Data Mining in Network Engineering'Bayesian Networks for Data Mining." In International Conference on Education, Management, Commerce and Society. Atlantis Press, 2015. http://dx.doi.org/10.2991/emcs-15.2015.84.

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Motoryn, Ruslan, Tetiana Motoryna, and Kateryna Prykhodko. "Impact of big data on development of the curriculums of training statisticians in Ukrainian university." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17702.

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In the Soviet era in Ukraine, theoretical subjects dominated in the education curriculum. Since independence, there have been profound changes in the higher education system of Ukraine. The system has adapted to the market model, with an increasing number of applied disciplines such as data mining, software engineering and data visualization. Courses should cover both theoretical knowledge and practical skills. Recently, one of the top requested areas at the information technology market is the processing of large data sets (Big data). Many leading universities of Ukraine, including Taras Shev
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Hauke, Krzysztof, Mievzyslaw L. Owoc, and Maciej Pondel. "Building Data Mining Models in the Oracle 9i Environment." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2697.

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Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressi
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Alawi, Sultan Juma Sultan, Izwan Nizal Mohd Shaharanee, and Jastini Mohd Jamil. "Profiling Oman education data using data mining approach." In THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17). Author(s), 2017. http://dx.doi.org/10.1063/1.5005467.

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Informes sobre el tema "Educative data mining"

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Zelinska, Snizhana O., Albert A. Azaryan, and Volodymyr A. Azaryan. 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. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2672.

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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 aca
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business
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Vizmanos, Jana Flor, Sheila Siar, Jose Ramon Albert, Janina Luz Sarmiento, and Angelo Hernandez. Like, Comment, and Share: Analyzing Public Sentiments of Government Policies in Social Media. Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.33.

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Social media has become an increasingly important tool for gauging public sentiment, offering real-time insights that can guide policy decisions. This study focuses on analyzing sentiments expressed on the Philippine Institute for Development Studies (PIDS) Facebook page, providing a window into public opinion on various development issues and governmental policies. By conducting opinion mining and sentiment analysis on comments from the top three viral Facebook posts of PIDS, which discuss education, the middle class, and social protection policies, the study reveals a range of public perspec
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Córdoba Solano, Daniela, Felipe Gomez-Trejos, and Alberto Vindas Quesada. Market Power, Industry Concentration and Trade Liberalization: Evidence from Costa Rica. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013429.

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Research about trade liberalization's impact on markups has focused on manufacturing due to data availability considerations. How do these effects vary across sectors? Which industries become more and less competitive as trade barriers are eliminated? We leverage firm-level tax records from the universe of formal-sector businesses in Costa Rica with the 2009 trade liberalization as a natural experiment to evaluate its industry-specific effects on markups across all industries. We find negative effects on markups in agriculture, mining, electricity, water supply, and business services. Alternat
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5

de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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