TY - GEN
T1 - Quantifying the mismatch between course content and students' dialogue in online learning environments
AU - Lim, Sunghoon
AU - Tucker, Conrad S.
AU - Jablokow, Kathryn
AU - Pursel, Bart
N1 - Publisher Copyright:
© 2017 ASME.
PY - 2017
Y1 - 2017
N2 - Due to the internet's increasing global availability, online learning has become a new paradigm for distance learning in higher education. While student interactions and reactions are readily observable in a physical classroom environment, monitoring student interactions and quantifying divergence between lecture topics and the topics that interest students are challenging in online learning platforms. Understanding the effects of this divergence is important for monitoring student engagement and aiding instructors, who are focused on improving the quality of their online courses. The authors of this paper propose a topic modeling method, based on latent Dirichlet allocation (LDA), that quantifies the effects of divergence between course topics (mined from textual transcriptions) and studentdiscussed topics (mined from discussion forums). Correlations between the measured dissimilarities and (a) the number of posts and comments in discussion forums, (b) the number of submitted assignments, and (c) students' average performance scores are presented. A case study involving video lecture transcripts and discussion forum posts/comments in a massive open online course (MOOC) platform demonstrates the proposed method's potential success and informs course providers about the challenges of measuring the topics that interest students.
AB - Due to the internet's increasing global availability, online learning has become a new paradigm for distance learning in higher education. While student interactions and reactions are readily observable in a physical classroom environment, monitoring student interactions and quantifying divergence between lecture topics and the topics that interest students are challenging in online learning platforms. Understanding the effects of this divergence is important for monitoring student engagement and aiding instructors, who are focused on improving the quality of their online courses. The authors of this paper propose a topic modeling method, based on latent Dirichlet allocation (LDA), that quantifies the effects of divergence between course topics (mined from textual transcriptions) and studentdiscussed topics (mined from discussion forums). Correlations between the measured dissimilarities and (a) the number of posts and comments in discussion forums, (b) the number of submitted assignments, and (c) students' average performance scores are presented. A case study involving video lecture transcripts and discussion forum posts/comments in a massive open online course (MOOC) platform demonstrates the proposed method's potential success and informs course providers about the challenges of measuring the topics that interest students.
UR - http://www.scopus.com/inward/record.url?scp=85034651364&partnerID=8YFLogxK
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U2 - 10.1115/DETC2017-67339
DO - 10.1115/DETC2017-67339
M3 - Conference contribution
AN - SCOPUS:85034651364
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
Y2 - 6 August 2017 through 9 August 2017
ER -