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Zhengrong Deng; Hong Xiang; Weijun Tang; Hanlie Cheng; Qiang Qin – International Journal of Information and Communication Technology Education, 2024
This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education…
Descriptors: Partnerships in Education, School Business Relationship, College Students, Vocational Education
Mertens, Ute; Finn, Bridgid; Lindner, Marlit Annalena – Journal of Educational Psychology, 2022
Feedback is one of the most important factors for successful learning. Contemporary computer-based learning and testing environments allow the implementation of automated feedback in a simple and efficient manner. Previous meta-analyses suggest that different types of feedback are not equally effective. This heterogeneity might depend on learner…
Descriptors: Computer Assisted Testing, Feedback (Response), Electronic Learning, Network Analysis
Thompson, James J. – Measurement: Interdisciplinary Research and Perspectives, 2022
With the use of computerized testing, ordinary assessments can capture both answer accuracy and answer response time. For the Canadian Programme for the International Assessment of Adult Competencies (PIAAC) numeracy and literacy subtests, person ability, person speed, question difficulty, question time intensity, fluency (rate), person fluency…
Descriptors: Foreign Countries, Adults, Computer Assisted Testing, Network Analysis
Yu, Fu-Yun; Sung, Shannon – Interactive Learning Environments, 2016
This study examined the effects of identity revelation and concealment on the number of times students' work was assessed in an online peer assessment context. It also examined the underlying reasons guiding the assessor's targeting behavior. Two fifth-grade classes participated. The one-group pretest-posttest experimental research design coupled…
Descriptors: Foreign Countries, Elementary School Students, Grade 5, Student Evaluation
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
McClean, Stephen; Hagan, Paul; Morgan, Jason – Bioscience Education, 2010
Text messaging has gained widespread popularity in higher education as a communication tool and as a means of engaging students in the learning process. In this study we report on the use of text messaging in a large, year-one introductory chemistry module where students were encouraged to send questions and queries to a dedicated text number both…
Descriptors: Electronic Mail, Voting, Chemistry, Lecture Method
Lamb, Richard L. – ProQuest LLC, 2013
Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity…
Descriptors: Educational Games, Models, Artificial Intelligence, Neurological Organization
Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction

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