Littérature scientifique sur le sujet « Student Outcome Prediction »
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Articles de revues sur le sujet "Student Outcome Prediction"
Mohd Talib, Nur Izzati, Nazatul Aini Abd Majid et Shahnorbanun Sahran. « Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine ». Applied Sciences 13, no 5 (3 mars 2023) : 3267. http://dx.doi.org/10.3390/app13053267.
Texte intégralIssaro, Sasitorn, et Panita Wannapiroon. « Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment ». International Journal of Emerging Technologies in Learning (iJET) 18, no 04 (23 février 2023) : 66–87. http://dx.doi.org/10.3991/ijet.v18i04.32583.
Texte intégralRoberts, Scott L. « Keep’em Guessing : Using Student Predictions to Inform Historical Understanding and Empathy ». Social Studies Research and Practice 11, no 3 (1 novembre 2016) : 45–50. http://dx.doi.org/10.1108/ssrp-03-2016-b0004.
Texte intégralHarwati, Defi Sri, et Heri Yanto. « Vocational High School (SMK) Students Accounting Competence Prediction Model by Using Astin I-E-O Model ». Dinamika Pendidikan 12, no 2 (1 mars 2018) : 98–113. http://dx.doi.org/10.15294/dp.v12i2.10826.
Texte intégralP S, Ambili, et Biku Abraham. « A Predictive Model for Student Employability Using Deep Learning Techniques ». ECS Transactions 107, no 1 (24 avril 2022) : 10149–58. http://dx.doi.org/10.1149/10701.10149ecst.
Texte intégralKhan, Ijaz Muhammad, Abdul Rahim Ahmad, Nafaa Jabeur et 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, no 15 (11 août 2021) : 4. http://dx.doi.org/10.3991/ijim.v15i15.20019.
Texte intégralGhodke, Keerti. « Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm ». International Journal for Research in Applied Science and Engineering Technology 10, no 7 (31 juillet 2022) : 3721–27. http://dx.doi.org/10.22214/ijraset.2022.45884.
Texte intégralBehr, Andreas, Marco Giese, Herve D. Teguim K et Katja Theune. « Early Prediction of University Dropouts – A Random Forest Approach ». Jahrbücher für Nationalökonomie und Statistik 240, no 6 (11 février 2020) : 743–89. http://dx.doi.org/10.1515/jbnst-2019-0006.
Texte intégralPan, Feng, Bingyao Huang, Chunhong Zhang, Xinning Zhu, Zhenyu Wu, Moyu Zhang, Yang Ji, Zhanfei Ma et Zhengchen Li. « A survival analysis based volatility and sparsity modeling network for student dropout prediction ». PLOS ONE 17, no 5 (5 mai 2022) : e0267138. http://dx.doi.org/10.1371/journal.pone.0267138.
Texte intégralNyompa, Sukri, Suprapta Suprapta, Sri Wahyuni et 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, no 2 (1 février 2018) : 131. http://dx.doi.org/10.26858/ugj.v1i2.6597.
Texte intégralThèses sur le sujet "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.
Texte intégralPredy, 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.
Texte intégralBleecker, 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.
Texte intégralJohnston, 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.
Texte intégralPh.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.
Texte intégralWood, Robert G. « Predicting the outcome of leadership identification from a college student's experiences ». W&M ScholarWorks, 2005. https://scholarworks.wm.edu/etd/1550154193.
Texte intégralWang, 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.
Texte intégralWood, 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.
Texte intégralFaas, 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.
Texte intégralPh. 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.
Texte intégralLivres sur le sujet "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.
Trouver le texte intégralSchmitt, Neal. Combining Cognitive and Noncognitive Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.003.0012.
Texte intégralChapitres de livres sur le sujet "Student Outcome Prediction"
Wang, Tianqi, Fenglong Ma, Tang Tang, Longfei Zhang et Jing Gao. « Textbook Enhanced Student Learning Outcome Prediction ». Dans 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.
Texte intégralWang, Tianqi, Fenglong Ma, Yaqing Wang, Tang Tang, Longfei Zhang et Jing Gao. « Towards Learning Outcome Prediction via Modeling Question Explanations and Student Responses ». Dans 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.
Texte intégralKubayi, Shiluva Claudia, Ashwini Jadhav et Ritesh Ajoodha. « A Machine Learning Approach for Predicting Students’ Second-Year Outcomes ». Dans Algorithms for Intelligent Systems, 535–47. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_41.
Texte intégralZaporozhko, Veronika V., Denis I. Parfenov et Vladimir M. Shardakov. « Development Approach of Formation of Individual Educational Trajectories Based on Neural Network Prediction of Student Learning Outcomes ». Dans 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.
Texte intégralDe Witte, Kristof, et Marc-André Chénier. « Learning Analytics in Education for the Twenty-First Century ». Dans 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.
Texte intégralKavitha, R. K., N. Jayakanthan et S. Harishma. « Predicting Students’ Outcomes with Respect to Trust, Perception, and Usefulness of Their Instructors in Academic Help Seeking Using Fuzzy Logic Approach ». Dans 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.
Texte intégralJärvinen, Tero, Jenni Tikkanen et Piia af Ursin. « The Significance of Socioeconomic Background for the Educational Dispositions and Aspirations of Finnish School Leavers ». Dans Finland’s Famous Education System, 243–56. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8241-5_15.
Texte intégralDianah, Siti, Ali Selamat et Ondrej Krejcar. « Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction ». Dans 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.
Texte intégralAslam, M. M. Haris, Ahmed F. Siddiqi, Khuram Shahzad et Sami Ullah Bajwa. « Predicting Student Academic Performance ». Dans Business Intelligence, 1445–62. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch070.
Texte intégralAlcolea, Juan J., Alvaro Ortigosa, Rosa M. Carro et Oscar J. Blanco. « Best Practices in Dropout Prediction ». Dans 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.
Texte intégralActes de conférences sur le sujet "Student Outcome Prediction"
Felix, Igor, Ana Paula Ambrósio, PRISCILA DA SILVA LIMA et Jacques Duílio Brancher. « Data Mining for Student Outcome Prediction on Moodle : a systematic mapping ». Dans 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.
Texte intégralSahu, Devesh, Rishi Sharma, Devesh Bharti et Utkarsh Narain Srivastava. « Control Algorithm for Anti-Lock Braking System ». Dans ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64640.
Texte intégralHu, Han, et Connor Heo. « Integration of Data Science Into Thermal-Fluids Engineering Education ». Dans ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-88193.
Texte intégralMcKillop, Conor. « Predicting the Outcome of Deliberative Democracy : A Research Proposal ». Dans 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.
Texte intégralKotova, Elena E., et Andrei S. Pisarev. « Adaptive prediction of student learning outcomes in online mode ». Dans 2017 IEEE II International Conference on Control in Technical Systems (CTS). IEEE, 2017. http://dx.doi.org/10.1109/ctsys.2017.8109509.
Texte intégralSimjanoska, Monika, Marjan Gusev, Sasko Ristov et Ana Madevska Bogdanova. « Intelligent student profiling for predicting e-Assessment outcomes ». Dans 2014 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2014. http://dx.doi.org/10.1109/educon.2014.6826157.
Texte intégralEveloy, Valerie, Shrinivas Bojanampati et Peter Rodgers. « Teaching of Beam Deflection Analysis Through Laboratory Experiments ». Dans ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65195.
Texte intégralSchoeffel, Pablo, Vinicius Faria Culmant Ramos et Raul Sidnei Wazlawick. « A Method to Predict At-risk Students in Introductory Computing Courses Based on Motivation ». Dans Workshops do Congresso Brasileiro de Informática na Educação. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/cbie.wcbie.2020.41.
Texte intégralNolte, Hannah, Catherine Berdanier, Jessica Menold et Christopher McComb. « Comparison of Exams and Design Practica for Assessment in First Year Engineering Design Courses ». Dans 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.
Texte intégralPadilha, TPP, et R. Catrambone. « USE OF THE TENSORFLOW FRAMEWORK TO SUPPORT EDUCATIONAL PROBLEMS : A SYSTEMATIC MAPPING ». Dans The 7th International Conference on Education 2021. The International Institute of Knowledge Management, 2021. http://dx.doi.org/10.17501/24246700.2021.7133.
Texte intégralRapports d'organisations sur le sujet "Student Outcome Prediction"
Sowjanya, Dr Kaniti, Dr Bongu Srinivas et 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, février 2023. http://dx.doi.org/10.36106/ijar/1606016.
Texte intégralSandford, 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, novembre 2018. http://dx.doi.org/10.53328/vsgg2030.
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