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Download full textHelen Durand; Saad Balhasan – International Review of Research in Open and Distributed Learning, 2023
This work describes an international collaboration experience carried out between our process design and petroleum property evaluation courses. This collaboration was developed as part of a partnership between the American University of Ras Al Khaimah (AURAK) in Ras Al Khaimah, United Arab Emirates, and Wayne State University in Detroit, Michigan,…
Descriptors: Cooperative Learning, Engineering Education, Undergraduate Students, Foreign Countries
Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings
Zhang, Jingjing; Yang, Yehong; Barbera, Elena; Lu, Yu – International Review of Research in Open and Distributed Learning, 2022
In massive open online courses (MOOCs), recommendation relationships present a collection of associations that imply a new form of integration, such as an interdisciplinary synergy among diverse disciplines. This study took a computer science approach, using the susceptible-infected (SI) model to simulate the process of learners accessing courses…
Descriptors: Network Analysis, Interdisciplinary Approach, MOOCs, Electronic Learning
Yunze He; Meiyou Ding; Min Liu; Leyang Wu; Zheyuan Liu; Haiyan Guo; Hongjin Wang – International Review of Research in Open and Distributed Learning, 2024
Sensing and Detection Technology is a core course in engineering specialties. Traditional sensor teaching methods have obvious deficiencies in cultivating students' ability. To better foster students' comprehensive qualities, this study explored a 4D1P (Four Dimensions and One Penetration) teaching mode. We independently developed an industrial…
Descriptors: Engineering Education, Engineering Technology, Teaching Methods, Student Centered Learning
Li, Fengliang; Wang, Liang – International Review of Research in Open and Distributed Learning, 2021
Few empirical studies have analyzed the return to distance higher education in different academic disciplines. This study used quantitative methods, data from a nationwide survey, and Mincerian earnings function to analyze the return to distance higher education among different disciplines in China's labor market. Results were compared with the…
Descriptors: Foreign Countries, Distance Education, Intellectual Disciplines, Labor Market
