Literatura científica selecionada sobre o tema "Educative data mining"
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Artigos de revistas sobre o assunto "Educative data mining"
Chen, Jianhui, e Jing Zhao. "An Educational Data Mining Model for Supervision of Network Learning Process". International Journal of Emerging Technologies in Learning (iJET) 13, n.º 11 (9 de novembro de 2018): 67. http://dx.doi.org/10.3991/ijet.v13i11.9599.
Texto completo da fonteЗелінська, Сніжана, Альберт Азарян e 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 de dezembro de 2018): 263–75. http://dx.doi.org/10.31812/pedag.v51i0.3674.
Texto completo da fonteKumar, Raj. "Data Mining in Education: A Review". International Journal Of Mechanical Engineering And Information Technology 05, n.º 01 (26 de janeiro de 2017): 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.
Texto completo da fonteZerlina, Dessy, Indarti Komala Dewi e 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, n.º 1 (1 de abril de 2020): 40–47. http://dx.doi.org/10.33751/injast.v1i1.1974.
Texto completo da fonteBunkar, Kamal. "Educational Data Mining in Practice Literature Review". Journal of Advanced Research in Embedded System 07, n.º 01 (26 de março de 2020): 1–7. http://dx.doi.org/10.24321/2395.3802.202001.
Texto completo da fonteKoedinger, Kenneth R., Sidney D'Mello, Elizabeth A. McLaughlin, Zachary A. Pardos e Carolyn P. Rosé. "Data mining and education". Wiley Interdisciplinary Reviews: Cognitive Science 6, n.º 4 (29 de abril de 2015): 333–53. http://dx.doi.org/10.1002/wcs.1350.
Texto completo da fonteRomero, Cristobal, e Sebastian Ventura. "Data mining in education". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, n.º 1 (14 de dezembro de 2012): 12–27. http://dx.doi.org/10.1002/widm.1075.
Texto completo da fonteK, Shilpa, e Krishna Prasad K. "A Study on Data Mining Techniques to Improve Students Performance in Higher Education". International Journal of Science and Research (IJSR) 12, n.º 10 (5 de outubro de 2023): 1287–92. http://dx.doi.org/10.21275/sr231014155301.
Texto completo da fonteАлисултанова, Э. Д., Л. К. Хаджиева e З. А. Шудуева. "DATA MINING TECHNIQUES IN EDUCATION". Вестник ГГНТУ. Гуманитарные и социально-экономические науки, n.º 2(28) (26 de agosto de 2022): 47–54. http://dx.doi.org/10.34708/gstou.2022.16.83.006.
Texto completo da fonteDwivedi, Shivendra, e 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, n.º 5 (1 de julho de 2018): 96–102. http://dx.doi.org/10.29070/15/57537.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteManspeaker, Rachel Bechtel. "Using data mining to differentiate instruction in college algebra". Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8542.
Texto completo da fonteDepartment 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.
Texto completo da fonteBurley, 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/.
Texto completo da fonteКузіков, Борис Олегович, Борис Олегович Кузиков e 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.
Texto completo da fontePepe, 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.
Texto completo da fontePh.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/.
Texto completo da fonteAllègre, Olivier. "Adapting the Prerequisite Structure to the Learner in Student Modeling". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS116.
Texto completo da fonteData-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.
Texto completo da fonteПетренко, 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.
Texto completo da fonteLivros sobre o assunto "Educative data mining"
Khan, Badrul H., Joseph Rene Corbeil e Maria Elena Corbeil, eds. Responsible Analytics and Data Mining in Education. New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9780203728703.
Texto completo da fonteHandbook of educational data mining. Boca Raton: Taylor & Francis Group, 2011.
Encontre o texto completo da fonteLinking competence to opportunities to learn: Models of competence and data mining. [ New York]: Springer, 2009.
Encontre o texto completo da fonteservice), SpringerLink (Online, ed. Modern Issues and Methods in Biostatistics. New York, NY: Springer Science+Business Media, LLC, 2011.
Encontre o texto completo da fonteMaine. 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. Augusta, Maine (P.O. Box 309, Augusta 04332-0309): The Division, 1992.
Encontre o texto completo da fonteRodrigues, Lopes Lia Carrari, Barretto Saulo Faria Almeida e SpringerLink (Online service), eds. Digital Ecosystems: Third International Conference, OPAALS 2010, Aracuju, Sergipe, Brazil, March 22-23, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Encontre o texto completo da fonteUnited 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.
Encontre o texto completo da fonteKolo, 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.
Encontre o texto completo da fonteIlias, Maglogiannis, Papadopoulos Harris e SpringerLink (Online service), eds. 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.
Encontre o texto completo da fonteIlias, Maglogiannis, Papadopoulos Harris, Karatzas Kostas, Sioutas Spyros e SpringerLink (Online service), eds. 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.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Educative data mining"
Guruler, Huseyin, e Ayhan Istanbullu. "Modeling Student Performance in Higher Education Using Data Mining". In Educational Data Mining, 105–24. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_4.
Texto completo da fonteAgrawal, Rakesh. "Enriching Education through Data Mining". In 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.
Texto completo da fonteSchönbrunn, Karoline, e Andreas Hilbert. "Data Mining in Higher Education". In 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.
Texto completo da fonteAgrawal, Rakesh, Sreenivas Gollapudi, Anitha Kannan e Krishnaram Kenthapadi. "Enriching Education through Data Mining". In 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.
Texto completo da fontePeña-Ayala, Alejandro, e Leonor Cárdenas. "How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study". In Educational Data Mining, 65–101. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_3.
Texto completo da fonteContreras Bravo, Leonardo Emiro, Giovanny Mauricio Tarazona Bermudez e José Ignacio Rodríguez Molano. "Big Data: An Exploration Toward the Improve of the Academic Performance in Higher Education". In Data Mining and Big Data, 627–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_59.
Texto completo da fonteEubanks, David, William Evers e Nancy Smith. "FINDING PREDICTORS IN HIGHER EDUCATION". In Data Mining and Learning Analytics, 41–53. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.ch3.
Texto completo da fonteOgrezeanu, Andreea-Elena. "Data Mining in Smart Agriculture". In Education, Research and Business Technologies, 249–57. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8866-9_21.
Texto completo da fonteAgarwal, Sonali, Murli Dhar Tiwari e Iti Tiwari. "Government Data Mining Case Studies on Education and Health". In E Governance Data Center, Data Warehousing and Data Mining, 155–201. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.
Texto completo da fonteOsorio-Acosta, Estefania. "Data Mining for Educational Management". In 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Educative data mining"
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.
Texto completo da fonteNduwimfura, Philbert, Yassein Nkoma e Zheng JianGuo. "Improving education through data mining". In 2013 International Conference on Information and Communication Technology for Education. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icte130301.
Texto completo da fonteYing Wah, Teh, e 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.
Texto completo da fonteMotoryn, Ruslan, Tetiana Motoryna e 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.
Texto completo da fonteWang, Xiaodan. "Data Mining in Network Engineering'Bayesian Networks for Data Mining". In International Conference on Education, Management, Commerce and Society. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/emcs-15.2015.84.
Texto completo da fonteHauke, Krzysztof, Mievzyslaw L. Owoc e 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.
Texto completo da fonteAlawi, Sultan Juma Sultan, Izwan Nizal Mohd Shaharanee e 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.
Texto completo da fonteR. P, Arya, e Anuja S. B. "Effectively Analysis and Predict Students Performance and Other Evaluation". In 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.
Texto completo da fonteOthman, El Harrak, Slimani Abdelali e El Bouhdidi Jaber. "Education data mining: Mining MOOCs videos using metadata based approach". In 2016 4th IEEE International Colloquium on Information Science and Technology (CIST). IEEE, 2016. http://dx.doi.org/10.1109/cist.2016.7805106.
Texto completo da fonteJuškaite, Loreta. "DATA MINING IN EDUCATION: ONLINE TESTING IN LATVIAN SCHOOLS". In 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.86.
Texto completo da fonteRelatórios de organizações sobre o assunto "Educative data mining"
Zelinska, Snizhana O., Albert A. Azaryan e 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.], novembro de 2018. http://dx.doi.org/10.31812/123456789/2672.
Texto completo da fonteVolkova, Nataliia P., Nina O. Rizun e Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], setembro de 2019. http://dx.doi.org/10.31812/123456789/3241.
Texto completo da fontede 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.
Texto completo da fontede 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.
Texto completo da fonte