MOOCs Meet Measurement Theory: A Topic-Modelling Approach

作者: Benjamin I. P. Rubinstein , Jiazhen He , James Bailey , Sandra Milligan , Jeffrey Chan

DOI:

关键词:

摘要: This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical for quantifying a person's attainment intangible attributes such as attitudes, abilities or intelligence. Such infer latent skill levels by relating them individuals' observed responses series items quiz questions. The set can be used measure if conform Guttman scale. well-scaled differentiate between individuals inferred span entire range most basic advanced. In practice, researchers manually devise (quiz questions) while optimising conformance. Due costly nature expert requirements this process, has found limited use in everyday teaching. We aim develop usable measurement highly-instrumented delivery platforms, using participation automatically-extracted topics items. challenge is formalise scale educational constraint incorporate it into models. To favour that automatically scale, we introduce novel regularisation non-negative matrix factorisation-based modelling. demonstrate suitability our approach with both quantitative experiments three Coursera MOOCs, qualitative survey interpretability two MOOCs domain interviews.

参考文章(22)
Daniel D. Lee, H. Sebastian Seung, Learning the parts of objects by non-negative matrix factorization Nature. ,vol. 401, pp. 788- 791 ,(1999) , 10.1038/44565
Rebecca Eynon, Nabeel Gillani, Michael A. Osborne, Stephen J. Roberts, Isis Hjorth, Communication Communities in MOOCs. arXiv: Computers and Society. ,(2014)
Bin Xu, Dan Yang, Study partners recommendation for xMOOCs learners Computational Intelligence and Neuroscience. ,vol. 2015, pp. 832093- 832093 ,(2015) , 10.1155/2015/832093
René F. Kizilcec, Chris Piech, Emily Schneider, Deconstructing disengagement: analyzing learner subpopulations in massive open online courses learning analytics and knowledge. pp. 170- 179 ,(2013) , 10.1145/2460296.2460330
Diyi Yang, Miaomiao Wen, Iris Howley, Robert Kraut, Carolyn Rose, Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses learning at scale. pp. 121- 130 ,(2015) , 10.1145/2724660.2724677
Diyi Yang, David Adamson, Carolyn Penstein Rosé, Question recommendation with constraints for massive open online courses conference on recommender systems. pp. 49- 56 ,(2014) , 10.1145/2645710.2645748
Anthony C. Robinson, Exploring Class Discussions from a Massive Open Online Course (MOOC) on Cartography International cartographic conference, CARTOCON 2014. pp. 173- 182 ,(2015) , 10.1007/978-3-319-07926-4_14
Sandra Milligan, Crowd-sourced learning in MOOCs: learning analytics meets measurement theory learning analytics and knowledge. pp. 151- 155 ,(2015) , 10.1145/2723576.2723596
Jonathan Huang, Andrew Ng, Chris Piech, Chuong Do, Zhenghao Chen, Daphne Koller, Tuned Models of Peer Assessment in MOOCs arXiv: Learning. ,(2013)