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Geller, Shay A.; Gal, Kobi; Segal, Avi; Sripathi, Kamali; Kim, Hyunsoo G.; Facciotti, Marc T.; Igo, Michele; Hoernle, Nicholas; Karger, David – IEEE Transactions on Learning Technologies, 2021
This article provides computational and rule-based approaches for detecting confusion that is expressed in students' comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is…
Descriptors: Identification, Misconceptions, Student Attitudes, Computer Mediated Communication
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Chen, Xin; Vorvoreanu, Mihaela; Madhavan, Krishna – IEEE Transactions on Learning Technologies, 2014
Students' informal conversations on social media (e.g., Twitter, Facebook) shed light into their educational experiences--opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity…
Descriptors: Social Media, Data Analysis, Sleep, Engineering Education
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Liu, Ming; Li, Yi; Xu, Weiwei; Liu, Li – IEEE Transactions on Learning Technologies, 2017
Writing an essay is a very important skill for students to master, but a difficult task for them to overcome. It is particularly true for English as Second Language (ESL) students in China. It would be very useful if students could receive timely and effective feedback about their writing. Automatic essay feedback generation is a challenging task,…
Descriptors: Foreign Countries, College Students, Second Language Learning, English (Second Language)