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Richards, K. Andrew R.; Velasquez, Juan D. – Journal on Excellence in College Teaching, 2014
Constructivist approaches to education embrace students' prior learning experiences and preference for learning in social environments. However, many postsecondary classes continue to embrace lecture-styles of teaching. This study sought to understand first-year students' perceptions of the mistakes instructors make in large lecture classes.…
Descriptors: Student Attitudes, Lecture Method, Large Group Instruction, Learner Engagement
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Inoue-Smith, Yukiko – Cogent Education, 2016
This study reexamined PowerPoint's potential to enhance traditional pedagogical practices in higher education. The study addressed (1) the conditions under which PowerPoint meets students' needs in typical lecture-based classrooms, (2) whether professors consider PowerPoint-based lectures more effective than lectures supported by material on…
Descriptors: College Faculty, Teaching Methods, Visual Aids, Computer Software
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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
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection