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Toward deep learning for adult students in online courses


Internet and Higher Education Volume 12, Number 3, ISSN 1096-7516 Publisher: Elsevier Ltd


Adult students have become the new majority in online distance education. Research in online distance education, however, is still predominantly based on the historical perspective of the traditional student profile. This study examines adult students' learning engagement in online courses and explores the impact of online course design models and the type of online discussion on adult students' self-perceived and observable learning performance. The study findings inform that age itself does not predict adult students' learning satisfaction and performance. Instead, an integrated course model promotes learning satisfaction, while a “Content”+“Support” course model reinforces knowledge–constructive online interactions. The study findings also indicate disadvantages of close-ended discussion tasks in supporting students' online learning success.


Ke, F. & Xie, K. (2009). Toward deep learning for adult students in online courses. Internet and Higher Education, 12(3), 136-145. Elsevier Ltd. Retrieved April 26, 2019 from .

This record was imported from Internet and Higher Education on February 1, 2019. Internet and Higher Education is a publication of Elsevier.

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