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Huimin Yang – International Journal of Web-Based Learning and Teaching Technologies, 2025
In view of the traditional teaching mode, this study focuses on the application of big data in personalized English teaching in colleges and universities. It aims at improving the teaching effect by using big data. By combining personalized teaching and big data theory, this paper analyzes the current situation of college English teaching,…
Descriptors: Data Use, Second Language Instruction, Teaching Methods, English (Second Language)
Dzakiria, Hisham; Razak, Asmahan Abdul; Mohamed, Abdul Halim – Online Submission, 2004
Literature on distance education and teacher education seems to show that what we do not know about Distance Teacher Trainees (DTT) and their learning process involved exceeds what we know about it. As more DTT enroll in distance education programmes globally, distance education providers and institutions will witness trainees coming with…
Descriptors: Teaching Methods, Foreign Countries, Learning Processes, Instructional Materials
Peer reviewedWiesenberg, Faye P. – Canadian Journal of Native Education, 1992
Learning outcomes of Alberta Native and non-Native adult learners were differentially affected by relative preference for visual versus verbal learning style. Also demonstrated were the importance of individual differences and the complex interaction of learning style preference, instructional materials, learning task, and cultural background.…
Descriptors: Adult Students, American Indians, Canada Natives, Cognitive Style
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

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