Literatura académica sobre el tema "Student Outcome Prediction"
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Artículos de revistas sobre el tema "Student Outcome Prediction"
Mohd Talib, Nur Izzati, Nazatul Aini Abd Majid y Shahnorbanun Sahran. "Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine". Applied Sciences 13, n.º 5 (3 de marzo de 2023): 3267. http://dx.doi.org/10.3390/app13053267.
Texto completoIssaro, Sasitorn y Panita Wannapiroon. "Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment". International Journal of Emerging Technologies in Learning (iJET) 18, n.º 04 (23 de febrero de 2023): 66–87. http://dx.doi.org/10.3991/ijet.v18i04.32583.
Texto completoRoberts, Scott L. "Keep’em Guessing: Using Student Predictions to Inform Historical Understanding and Empathy". Social Studies Research and Practice 11, n.º 3 (1 de noviembre de 2016): 45–50. http://dx.doi.org/10.1108/ssrp-03-2016-b0004.
Texto completoHarwati, Defi Sri y Heri Yanto. "Vocational High School (SMK) Students Accounting Competence Prediction Model by Using Astin I-E-O Model". Dinamika Pendidikan 12, n.º 2 (1 de marzo de 2018): 98–113. http://dx.doi.org/10.15294/dp.v12i2.10826.
Texto completoP S, Ambili y Biku Abraham. "A Predictive Model for Student Employability Using Deep Learning Techniques". ECS Transactions 107, n.º 1 (24 de abril de 2022): 10149–58. http://dx.doi.org/10.1149/10701.10149ecst.
Texto completoKhan, Ijaz Muhammad, Abdul Rahim Ahmad, Nafaa Jabeur y Mohammed Najah Mahdi. "A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models". International Journal of Interactive Mobile Technologies (iJIM) 15, n.º 15 (11 de agosto de 2021): 4. http://dx.doi.org/10.3991/ijim.v15i15.20019.
Texto completoGhodke, Keerti. "Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm". International Journal for Research in Applied Science and Engineering Technology 10, n.º 7 (31 de julio de 2022): 3721–27. http://dx.doi.org/10.22214/ijraset.2022.45884.
Texto completoBehr, Andreas, Marco Giese, Herve D. Teguim K y Katja Theune. "Early Prediction of University Dropouts – A Random Forest Approach". Jahrbücher für Nationalökonomie und Statistik 240, n.º 6 (11 de febrero de 2020): 743–89. http://dx.doi.org/10.1515/jbnst-2019-0006.
Texto completoPan, Feng, Bingyao Huang, Chunhong Zhang, Xinning Zhu, Zhenyu Wu, Moyu Zhang, Yang Ji, Zhanfei Ma y Zhengchen Li. "A survival analysis based volatility and sparsity modeling network for student dropout prediction". PLOS ONE 17, n.º 5 (5 de mayo de 2022): e0267138. http://dx.doi.org/10.1371/journal.pone.0267138.
Texto completoNyompa, Sukri, Suprapta Suprapta, Sri Wahyuni y Muhamad Ihsan Azhim. "The Effect of Student Perception of Teacher Professional Competency On The Result of Geography Learning Class XI Social Science Student’s SMA 12 Sinjai". UNM Geographic Journal 1, n.º 2 (1 de febrero de 2018): 131. http://dx.doi.org/10.26858/ugj.v1i2.6597.
Texto completoTesis sobre el tema "Student Outcome Prediction"
Sandusky, Sue Ann. "Predicting Student Veteran Persistence". Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1585070424571773.
Texto completoPredy, Larissa Kristine. "Predicting student outcomes using office referral data from a national sample of middle school students". Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43817.
Texto completoBleecker, Wendy S. "Predicting student outcomes for Washington State middle schools using school counselor's and administrator's racial consciousness and organizational variables". Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Fall2007/w_bleecker_113007.pdf.
Texto completoJohnston, Jaures Prescott. "Predicting Educational Outcomes For Students Returning From Incarceration". Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/42850.
Texto completoPh.D.
During the 2005-2006 school year, 967 students returned from incarceration and were assigned to RETI-WRAP (Re-Entry Transition Initiative-Welcome Return Assessment Process), a ten-day transition program operated by the School District of Philadelphia designed to review, evaluate, and make recommendations for appropriate school placement upon their return to the public school system. The current study employed a retrospective analysis of archival data from the ’05-’06 school year in order to identify those variables that predict successful transition (active in school or graduated). The data included demographic information (e.g., gender, grade, high school credits, and race), educational placement (e.g., regular or special education), severity of crime and reading and math scores as determined by standardized testing conducted by RETI-WRAP personnel. Eight variables were used to determine the prevalence, relationships, and predictive power of demographic, academic, and crime-related variables. Frequency distributions, Pearson correlations, Phi coefficients, and discriminant function analysis were conducted to examine prevalence, associations between variables, and predictions to successful re-entry. A significant Wilks’ Lamba of .945 was obtained for the sole discriminant function. Three variables emerged as significant predictors of successful re-entry: the number of credits obtained, the severity of the crime committed, and the age of the student. Younger students with more credits who committed less severe crimes were more likely to have achieved a successful transition. The amount of variance (5%) explained by the statistical model was limited by the imbalanced nature of the sample, in that few students (21.9%) experienced a successful transition. The current study highlighted the dynamics and overall profile of one of the most challenging and vulnerable populations in the public school system. By using database decision- making and providing a comprehensive framework to understand the characteristics of students who transition successfully, policy makers are in a better position to identify an optimal placement match based on empirical findings, thus decreasing the number of students who drop out of school or who remain involved with the juvenile justice system.
Temple University--Theses
Allen, Patricia Hayden. "The relationship of learner entry characteristics and reading and writing skills to program exit outcome". FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/1141.
Texto completoWood, Robert G. "Predicting the outcome of leadership identification from a college student's experiences". W&M ScholarWorks, 2005. https://scholarworks.wm.edu/etd/1550154193.
Texto completoWang, Xueli. "From Access to Success: Factors Predicting the Educational Outcomes of Baccalaureate Aspirants Beginning at Community Colleges". Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1215015456.
Texto completoWood, Julie E. "Predicting School Success From A Disruption in Educational Experience". Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1477645391441543.
Texto completoFaas, Caitlin Suzanne. "Predicting Socioeconomic Success and Mental Health Outcomes for Young Adults who Dropped out of College". Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23934.
Texto completoPh. D.
Hardesty, Robin B. "Stress, Coping, and their Prediction of Mental Health Outcomes in International Baccalaureate High School Students". Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3869.
Texto completoLibros sobre el tema "Student Outcome Prediction"
Resource allocation and student achievement: A microlevel impact study of differential resource inputs on student achievement outcomes. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1996.
Buscar texto completoSchmitt, Neal. Combining Cognitive and Noncognitive Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.003.0012.
Texto completoCapítulos de libros sobre el tema "Student Outcome Prediction"
Wang, Tianqi, Fenglong Ma, Tang Tang, Longfei Zhang y Jing Gao. "Textbook Enhanced Student Learning Outcome Prediction". En Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 352–60. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977172.40.
Texto completoWang, Tianqi, Fenglong Ma, Yaqing Wang, Tang Tang, Longfei Zhang y Jing Gao. "Towards Learning Outcome Prediction via Modeling Question Explanations and Student Responses". En Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 693–701. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.78.
Texto completoKubayi, Shiluva Claudia, Ashwini Jadhav y Ritesh Ajoodha. "A Machine Learning Approach for Predicting Students’ Second-Year Outcomes". En Algorithms for Intelligent Systems, 535–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_41.
Texto completoZaporozhko, Veronika V., Denis I. Parfenov y Vladimir M. Shardakov. "Development Approach of Formation of Individual Educational Trajectories Based on Neural Network Prediction of Student Learning Outcomes". En Advances in Intelligent Systems and Computing, 305–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39162-1_28.
Texto completoDe Witte, Kristof y Marc-André Chénier. "Learning Analytics in Education for the Twenty-First Century". En Handbook of Computational Social Science for Policy, 305–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16624-2_16.
Texto completoKavitha, R. K., N. Jayakanthan y S. Harishma. "Predicting Students’ Outcomes with Respect to Trust, Perception, and Usefulness of Their Instructors in Academic Help Seeking Using Fuzzy Logic Approach". En Advancements in Smart Computing and Information Security, 233–43. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23092-9_19.
Texto completoJärvinen, Tero, Jenni Tikkanen y Piia af Ursin. "The Significance of Socioeconomic Background for the Educational Dispositions and Aspirations of Finnish School Leavers". En Finland’s Famous Education System, 243–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8241-5_15.
Texto completoDianah, Siti, Ali Selamat y Ondrej Krejcar. "Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction". En Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, 371–89. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8686-0.ch014.
Texto completoAslam, M. M. Haris, Ahmed F. Siddiqi, Khuram Shahzad y Sami Ullah Bajwa. "Predicting Student Academic Performance". En Business Intelligence, 1445–62. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch070.
Texto completoAlcolea, Juan J., Alvaro Ortigosa, Rosa M. Carro y Oscar J. Blanco. "Best Practices in Dropout Prediction". En Early Warning Systems and Targeted Interventions for Student Success in Online Courses, 301–23. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-5074-8.ch015.
Texto completoActas de conferencias sobre el tema "Student Outcome Prediction"
Felix, Igor, Ana Paula Ambrósio, PRISCILA DA SILVA LIMA y Jacques Duílio Brancher. "Data Mining for Student Outcome Prediction on Moodle: a systematic mapping". En XXIX Simpósio Brasileiro de Informática na Educação (Brazilian Symposium on Computers in Education). Brazilian Computer Society (Sociedade Brasileira de Computação - SBC), 2018. http://dx.doi.org/10.5753/cbie.sbie.2018.1393.
Texto completoSahu, Devesh, Rishi Sharma, Devesh Bharti y Utkarsh Narain Srivastava. "Control Algorithm for Anti-Lock Braking System". En ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64640.
Texto completoHu, Han y Connor Heo. "Integration of Data Science Into Thermal-Fluids Engineering Education". En ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-88193.
Texto completoMcKillop, Conor. "Predicting the Outcome of Deliberative Democracy: A Research Proposal". En Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-2013.
Texto completoKotova, Elena E. y Andrei S. Pisarev. "Adaptive prediction of student learning outcomes in online mode". En 2017 IEEE II International Conference on Control in Technical Systems (CTS). IEEE, 2017. http://dx.doi.org/10.1109/ctsys.2017.8109509.
Texto completoSimjanoska, Monika, Marjan Gusev, Sasko Ristov y Ana Madevska Bogdanova. "Intelligent student profiling for predicting e-Assessment outcomes". En 2014 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2014. http://dx.doi.org/10.1109/educon.2014.6826157.
Texto completoEveloy, Valerie, Shrinivas Bojanampati y Peter Rodgers. "Teaching of Beam Deflection Analysis Through Laboratory Experiments". En ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65195.
Texto completoSchoeffel, Pablo, Vinicius Faria Culmant Ramos y Raul Sidnei Wazlawick. "A Method to Predict At-risk Students in Introductory Computing Courses Based on Motivation". En Workshops do Congresso Brasileiro de Informática na Educação. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/cbie.wcbie.2020.41.
Texto completoNolte, Hannah, Catherine Berdanier, Jessica Menold y Christopher McComb. "Comparison of Exams and Design Practica for Assessment in First Year Engineering Design Courses". En ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22054.
Texto completoPadilha, TPP y R. Catrambone. "USE OF THE TENSORFLOW FRAMEWORK TO SUPPORT EDUCATIONAL PROBLEMS: A SYSTEMATIC MAPPING". En The 7th International Conference on Education 2021. The International Institute of Knowledge Management, 2021. http://dx.doi.org/10.17501/24246700.2021.7133.
Texto completoInformes sobre el tema "Student Outcome Prediction"
Sowjanya, Dr Kaniti, Dr Bongu Srinivas y Dr Metta Lakshmana Rao. A STUDY ON FIBROSCAN COMPARED TO AST TO PLATELET RATIO INDEX(APRI) FOR ASSESSMENT OF LIVER FIBROSIS WITH NONALCOHOLIC FATTY LIVER DISEASE(NAFLD). World Wide Journals, febrero de 2023. http://dx.doi.org/10.36106/ijar/1606016.
Texto completoSandford, Robert, Vladimir Smakhtin, Colin Mayfield, Hamid Mehmood, John Pomeroy, Chris Debeer, Phani Adapa et al. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, noviembre de 2018. http://dx.doi.org/10.53328/vsgg2030.
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