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