TY - CONF
T1 - Learner affect through the looking glass
T2 - 10th International Conference on Educational Data Mining, EDM 2017
AU - Zeng, Ziheng
AU - Chaturvedi, Snigdha
AU - Bhat, Suma
N1 - Funding Information:
This work is supported in part by the IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) – a research collaboration as part of the IBM Cognitive Horizons Network.
Funding Information:
This work is supported in part by the IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) ? a research collaboration as part of the IBM Cognitive Horizons Network.
Publisher Copyright:
© 2017 International Educational Data Mining Society. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Characterizing the nature of students’ affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needs–primarily seeking factual answers. We quantitatively demonstrate that the use of content-related linguistic features and community-related features derived from a post serve as reliable detectors of confusion while widely outperforming currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses.
AB - Characterizing the nature of students’ affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needs–primarily seeking factual answers. We quantitatively demonstrate that the use of content-related linguistic features and community-related features derived from a post serve as reliable detectors of confusion while widely outperforming currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses.
KW - Confusion characterization
KW - Discussion forum analysis
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M3 - Paper
AN - SCOPUS:85062792095
SP - 272
EP - 277
Y2 - 25 June 2017 through 28 June 2017
ER -