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Direct linkXinXiu Yang – International Journal of Information and Communication Technology Education, 2024
The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization)…
Descriptors: Prediction, Employment Patterns, College Students, Algorithms
Download full textMao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
Rozell, Timothy G.; Johnson, Jessica; Sexten, Andrea; Rhodes, Ashley E. – Journal of College Science Teaching, 2017
Students in a junior- and senior-level Anatomy and Physiology course have the opportunity to correct missed exam questions ("regrade") and earn up to half of the original points missed. The three objectives of this study were to determine if: (a) performance on the regrade assignment was correlated with scores on subsequent exams, (b)…
Descriptors: Physiology, Scores, Grades (Scholastic), Exit Examinations
Gray, Geraldine; McGuinness, Colm; Owende, Philip; Carthy, Aiden – Journal of Learning Analytics, 2014
Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and…
Descriptors: Psychometrics, Data Analysis, Academic Achievement, Postsecondary Education
McAleer, Brenda; Szakas, Joseph S. – Information Systems Education Journal, 2010
In the past few years, universities have become much more involved in outcomes assessment. Outside of the classroom analysis of learning outcomes, an investigation is performed into the use of current data mining tools to assess the issue of student retention within the Computer Information Systems (CIS) department. Utilizing both a historical…
Descriptors: College Students, Computer Science Education, Information Systems, Prior Learning
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Braun, Henry I. – 1988
Empirical Bayes (EB) methods are frequently used on hierarchical linear models in practice. This paper provides an overview of parametric EB methods with special emphasis on their application in data-analytic settings. Eight different models with different levels of complexity are described. Comparisons of performance with other methods are…
Descriptors: Bayesian Statistics, College Students, Data Analysis, Higher Education
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
