Littérature scientifique sur le sujet « Educative data mining »
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Articles de revues sur le sujet "Educative data mining"
Chen, Jianhui, et Jing Zhao. « An Educational Data Mining Model for Supervision of Network Learning Process ». International Journal of Emerging Technologies in Learning (iJET) 13, no 11 (9 novembre 2018) : 67. http://dx.doi.org/10.3991/ijet.v13i11.9599.
Texte intégralЗелінська, Сніжана, Альберт Азарян et 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 (13 décembre 2018) : 263–75. http://dx.doi.org/10.31812/pedag.v51i0.3674.
Texte intégralKumar, Raj. « Data Mining in Education : A Review ». International Journal Of Mechanical Engineering And Information Technology 05, no 01 (26 janvier 2017) : 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.
Texte intégralZerlina, Dessy, Indarti Komala Dewi et 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 (1 avril 2020) : 40–47. http://dx.doi.org/10.33751/injast.v1i1.1974.
Texte intégralBunkar, Kamal. « Educational Data Mining in Practice Literature Review ». Journal of Advanced Research in Embedded System 07, no 01 (26 mars 2020) : 1–7. http://dx.doi.org/10.24321/2395.3802.202001.
Texte intégralKoedinger, Kenneth R., Sidney D'Mello, Elizabeth A. McLaughlin, Zachary A. Pardos et Carolyn P. Rosé. « Data mining and education ». Wiley Interdisciplinary Reviews : Cognitive Science 6, no 4 (29 avril 2015) : 333–53. http://dx.doi.org/10.1002/wcs.1350.
Texte intégralRomero, Cristobal, et Sebastian Ventura. « Data mining in education ». Wiley Interdisciplinary Reviews : Data Mining and Knowledge Discovery 3, no 1 (14 décembre 2012) : 12–27. http://dx.doi.org/10.1002/widm.1075.
Texte intégralK, Shilpa, et 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 (5 octobre 2023) : 1287–92. http://dx.doi.org/10.21275/sr231014155301.
Texte intégralАлисултанова, Э. Д., Л. К. Хаджиева et З. А. Шудуева. « DATA MINING TECHNIQUES IN EDUCATION ». Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no 2(28) (26 août 2022) : 47–54. http://dx.doi.org/10.34708/gstou.2022.16.83.006.
Texte intégralDwivedi, Shivendra, et 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 (1 juillet 2018) : 96–102. http://dx.doi.org/10.29070/15/57537.
Texte intégralThèses sur le sujet "Educative data mining"
Войцун, О. Є. « Перспективи educational data mining в Україні ». Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47901.
Texte intégralManspeaker, Rachel Bechtel. « Using data mining to differentiate instruction in college algebra ». Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8542.
Texte intégralDepartment of Mathematics
Andrew G. Bennett
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 may then take advantage of this expedient analysis to adjust instruction to meet their students’ needs. Using exam problem grades, attendance points, and homework scores from the first four weeks of a Studio College Algebra class, the researchers were able to identify five distinct clusters of students. Interviews of prototypical students from each group revealed their motivations, level of conceptual understanding, and attitudes about mathematics. The student groups where then given the following descriptive names: Overachievers, Underachievers, Employees, Rote Memorizers, and Sisyphean Strivers. In order to improve placement of incoming students, new student services and student advisors across campus have been given profiles of the student clusters and placement suggestions. Preliminary evidence shows that advisors have been able to effectively identify members of these groups during their consultations and suggest the most appropriate math course for those students. In addition to placement suggestions, several targeted interventions are currently being developed to benefit underperforming groups of students. Each student group reacts differently to various elements of the course and assistance strategies. By identifying students who are likely to struggle within the first month of classes, and the recovery strategy that would be most effective, instructors can intercede in time to improve performance.
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.
Texte intégralBurley, 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/.
Texte intégralКузіков, Борис Олегович, Борис Олегович Кузиков et 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.
Texte intégralPepe, 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.
Texte intégralPh.D.
Department of Educational and Human Sciences
Education
Education PhD
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/.
Texte intégralAllègre, Olivier. « Adapting the Prerequisite Structure to the Learner in Student Modeling ». Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS116.
Texte intégralData-driven learner models aim to represent and understand students' knowledge and other meta-cognitive characteristics to support their learning by making predictions about their future performance. Learner modeling can be approached using various complex system models, each providing a different perspective on the student and the learning process. Knowledge-enhanced machine learning techniques, such as Bayesian networks, are particularly well suited for incorporating domain knowledge into the learner model, making them a valuable tool in student modeling.This work explores the modeling and the potential applications of a new framework, called E-PRISM, for Embedding Prerequisite Relationships In Student Modeling, which includes a learner model based on dynamic Bayesian networks. It uses a new architecture for Bayesian networks that rely on the clause of Independence of Causal Influences (ICI), which reduces the number of parameters in the network and allows enhanced interpretability. The study examines the strengths of E-PRISM, including its ability to consider the prerequisite structure between knowledge components, its limited number of parameters, and its enhanced interpretability. The study also introduces a novel approach for approximate inference in large ICI-based Bayesian networks, as well as a performant parameter learning algorithm in ICI-based Bayesian networks. Overall, the study demonstrates the potential of E-PRISM as a promising tool for discovering the prerequisite structure of domain knowledge that may be adapted to the learner with the perspective of improving the outer-loop adaptivity
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.
Texte intégralПетренко, 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.
Texte intégralLivres sur le sujet "Educative data mining"
Khan, Badrul H., Joseph Rene Corbeil et Maria Elena Corbeil, dir. Responsible Analytics and Data Mining in Education. New York, NY : Routledge, 2019. : Routledge, 2018. http://dx.doi.org/10.4324/9780203728703.
Texte intégralHandbook of educational data mining. Boca Raton : Taylor & Francis Group, 2011.
Trouver le texte intégralLinking competence to opportunities to learn : Models of competence and data mining. [ New York] : Springer, 2009.
Trouver le texte intégralservice), SpringerLink (Online, dir. Modern Issues and Methods in Biostatistics. New York, NY : Springer Science+Business Media, LLC, 2011.
Trouver le texte intégralMaine. Bureau of Employment Security., dir. Maine occupational staffing for selected nonmanufacturing industries : Mining, construction, finance, insurance, and real estate services, except hospital and education : data for second quarter 1990. Augusta, Maine (P.O. Box 309, Augusta 04332-0309) : The Division, 1992.
Trouver le texte intégralRodrigues, Lopes Lia Carrari, Barretto Saulo Faria Almeida et SpringerLink (Online service), dir. Digital Ecosystems : Third International Conference, OPAALS 2010, Aracuju, Sergipe, Brazil, March 22-23, 2010, Revised Selected Papers. Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2010.
Trouver le texte intégralUnited 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. Washington : U.S. Government Printing Office, 2015.
Trouver le texte intégralKolo, 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. [Minna ? : Niger State College of Education, 2003.
Trouver le texte intégralIlias, Maglogiannis, Papadopoulos Harris et SpringerLink (Online service), dir. Artificial Intelligence Applications and Innovations : 8th IFIP WG 12.5 International Conference, AIAI 2012, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part I. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
Trouver le texte intégralIlias, Maglogiannis, Papadopoulos Harris, Karatzas Kostas, Sioutas Spyros et SpringerLink (Online service), dir. Artificial Intelligence Applications and Innovations : AIAI 2012 International Workshops : AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
Trouver le texte intégralChapitres de livres sur le sujet "Educative data mining"
Guruler, Huseyin, et Ayhan Istanbullu. « Modeling Student Performance in Higher Education Using Data Mining ». Dans Educational Data Mining, 105–24. Cham : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_4.
Texte intégralAgrawal, Rakesh. « Enriching Education through Data Mining ». Dans Machine Learning and Knowledge Discovery in Databases, 1–2. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23780-5_1.
Texte intégralSchönbrunn, Karoline, et Andreas Hilbert. « Data Mining in Higher Education ». Dans Studies in Classification, Data Analysis, and Knowledge Organization, 489–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_56.
Texte intégralAgrawal, Rakesh, Sreenivas Gollapudi, Anitha Kannan et Krishnaram Kenthapadi. « Enriching Education through Data Mining ». Dans Lecture Notes in Computer Science, 1–2. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21786-9_1.
Texte intégralPeña-Ayala, Alejandro, et Leonor Cárdenas. « How Educational Data Mining Empowers State Policies to Reform Education : The Mexican Case Study ». Dans Educational Data Mining, 65–101. Cham : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_3.
Texte intégralContreras Bravo, Leonardo Emiro, Giovanny Mauricio Tarazona Bermudez et José Ignacio Rodríguez Molano. « Big Data : An Exploration Toward the Improve of the Academic Performance in Higher Education ». Dans Data Mining and Big Data, 627–37. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_59.
Texte intégralEubanks, David, William Evers et Nancy Smith. « FINDING PREDICTORS IN HIGHER EDUCATION ». Dans Data Mining and Learning Analytics, 41–53. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch3.
Texte intégralOgrezeanu, Andreea-Elena. « Data Mining in Smart Agriculture ». Dans Education, Research and Business Technologies, 249–57. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8866-9_21.
Texte intégralAgarwal, Sonali, Murli Dhar Tiwari et Iti Tiwari. « Government Data Mining Case Studies on Education and Health ». Dans E Governance Data Center, Data Warehousing and Data Mining, 155–201. New York : River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.
Texte intégralOsorio-Acosta, Estefania. « Data Mining for Educational Management ». Dans Encyclopedia of Education and Information Technologies, 487–93. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10576-1_124.
Texte intégralActes de conférences sur le sujet "Educative data mining"
Ponelis, Shana. « Finding Diamonds in Data : Reflections on Teaching Data Mining from the Coal Face ». Dans InSITE 2009 : Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3313.
Texte intégralNduwimfura, Philbert, Yassein Nkoma et Zheng JianGuo. « Improving education through data mining ». Dans 2013 International Conference on Information and Communication Technology for Education. Southampton, UK : WIT Press, 2014. http://dx.doi.org/10.2495/icte130301.
Texte intégralYing Wah, Teh, et Zaitun Abu Bakar. « Investigating the Status of Data Mining in Practice ». Dans 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2719.
Texte intégralMotoryn, Ruslan, Tetiana Motoryna et Kateryna Prykhodko. « Impact of big data on development of the curriculums of training statisticians in Ukrainian university ». Dans Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17702.
Texte intégralWang, Xiaodan. « Data Mining in Network Engineering'Bayesian Networks for Data Mining ». Dans International Conference on Education, Management, Commerce and Society. Paris, France : Atlantis Press, 2015. http://dx.doi.org/10.2991/emcs-15.2015.84.
Texte intégralHauke, Krzysztof, Mievzyslaw L. Owoc et Maciej Pondel. « Building Data Mining Models in the Oracle 9i Environment ». Dans 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2697.
Texte intégralAlawi, Sultan Juma Sultan, Izwan Nizal Mohd Shaharanee et Jastini Mohd Jamil. « Profiling Oman education data using data mining approach ». Dans THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17). Author(s), 2017. http://dx.doi.org/10.1063/1.5005467.
Texte intégralR. P, Arya, et Anuja S. B. « Effectively Analysis and Predict Students Performance and Other Evaluation ». Dans The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/gdhl6261/ngcesi23p2.
Texte intégralOthman, El Harrak, Slimani Abdelali et El Bouhdidi Jaber. « Education data mining : Mining MOOCs videos using metadata based approach ». Dans 2016 4th IEEE International Colloquium on Information Science and Technology (CIST). IEEE, 2016. http://dx.doi.org/10.1109/cist.2016.7805106.
Texte intégralJuškaite, Loreta. « DATA MINING IN EDUCATION : ONLINE TESTING IN LATVIAN SCHOOLS ». Dans 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.86.
Texte intégralRapports d'organisations sur le sujet "Educative data mining"
Zelinska, Snizhana O., Albert A. Azaryan et 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.], novembre 2018. http://dx.doi.org/10.31812/123456789/2672.
Texte intégralVolkova, Nataliia P., Nina O. Rizun et Maryna V. Nehrey. Data science : opportunities to transform education. [б. в.], septembre 2019. http://dx.doi.org/10.31812/123456789/3241.
Texte intégralde Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison 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.
Texte intégralde Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison 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.
Texte intégral