TY - GEN
T1 - Emotion and Associated Topic Detection for Course Comments in a MOOC Platform
AU - Liu, Zhi
AU - Zhang, Wenjing
AU - Sun, Jianwen
AU - Cheng, Hercy N.H.
AU - Peng, Xian
AU - Liu, Sanya
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Massive Open Online Course (MOOC) has been drawn much attention from learners and teachers through the world. MOOC offers a variety of interactive ways, in which the course comment panel is used for express students' opinions and feelings. These comments generally contain some learning problems, attitudes towards the course or the platform support, etc. The feedback information is beneficial for the exchange of ideas among teachers, learners and educational administrators. However, it is quite time-consuming to analyze these important opinions entirely by artificial reading. It is imperative that the MOOC needs the machine learning methods to detect the emotions and topics in text data. In this paper, we propose an application framework and design scheme of intelligent system for the emotion recognition and topic mining, aiming at conducting the intelligent and personalized learning analytics on MOOC. The purposes of the intelligent comment mining system include (1) predicting popularity level of each course, (2) obtaining emotion-topic feedbacks about content of courses for teachers to analyze and improve their teaching strategies, (3) obtaining emotion-topic feedbacks about platform support for administrators to improve user experiences in platform.
AB - Massive Open Online Course (MOOC) has been drawn much attention from learners and teachers through the world. MOOC offers a variety of interactive ways, in which the course comment panel is used for express students' opinions and feelings. These comments generally contain some learning problems, attitudes towards the course or the platform support, etc. The feedback information is beneficial for the exchange of ideas among teachers, learners and educational administrators. However, it is quite time-consuming to analyze these important opinions entirely by artificial reading. It is imperative that the MOOC needs the machine learning methods to detect the emotions and topics in text data. In this paper, we propose an application framework and design scheme of intelligent system for the emotion recognition and topic mining, aiming at conducting the intelligent and personalized learning analytics on MOOC. The purposes of the intelligent comment mining system include (1) predicting popularity level of each course, (2) obtaining emotion-topic feedbacks about content of courses for teachers to analyze and improve their teaching strategies, (3) obtaining emotion-topic feedbacks about platform support for administrators to improve user experiences in platform.
KW - course comments
KW - emotion recognition
KW - learning analytics
KW - Massive Open Online Course (MOOC)
KW - topic mining
UR - http://www.scopus.com/inward/record.url?scp=85015759873&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015759873&partnerID=8YFLogxK
U2 - 10.1109/EITT.2016.11
DO - 10.1109/EITT.2016.11
M3 - Conference contribution
AN - SCOPUS:85015759873
T3 - Proceedings - 5th International Conference on Educational Innovation through Technology, EITT 2016
SP - 15
EP - 19
BT - Proceedings - 5th International Conference on Educational Innovation through Technology, EITT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Educational Innovation through Technology, EITT 2016
Y2 - 22 September 2016 through 24 September 2016
ER -