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Download full textHew, Khe Foon; Qiao, Chen; Tang, Ying – International Review of Research in Open and Distributed Learning, 2018
Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884…
Descriptors: Learner Engagement, Large Group Instruction, Online Courses, Man Machine Systems
Amigud, Alexander; Arnedo-Moreno, Joan; Daradoumis, Thanasis; Guerrero-Roldan, Ana-Elena – International Review of Research in Open and Distributed Learning, 2017
This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students' patterns of language use from data,…
Descriptors: Data Collection, Data Analysis, Integrity, Electronic Learning
Veletsianos, George; Shepherdson, Peter – International Review of Research in Open and Distributed Learning, 2016
A deluge of empirical research became available on MOOCs in 2013-2015 and this research is available in disparate sources. This paper addresses a number of gaps in the scholarly understanding of MOOCs and presents a comprehensive picture of the literature by examining the geographic distribution, publication outlets, citations, data collection and…
Descriptors: Literature Reviews, Educational Technology, Online Courses, Large Group Instruction
