TY - GEN
T1 - Revealing Hidden Impression Topics in Students' Journals Based on Nonnegative Matrix Factorization
AU - Taniguchi, Yuta
AU - Suehiro, Daiki
AU - Ogata, Hiroaki
N1 - Funding Information:
ACKNOWLEDGMENT The research is supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” (178A03), the Commissioned Research of the National Institute of Information and Communications Technology (NICT), Japan; Grant-in-Aid for Scientific Research (S) No. 16H06304; and the Education Enhancement Program (EEP) of Kyushu University.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/3
Y1 - 2017/8/3
N2 - Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course 'Information Science' held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts.
AB - Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course 'Information Science' held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts.
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U2 - 10.1109/ICALT.2017.113
DO - 10.1109/ICALT.2017.113
M3 - Conference contribution
AN - SCOPUS:85030260591
T3 - Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
SP - 298
EP - 300
BT - Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
A2 - Huang, Ronghuai
A2 - Vasiu, Radu
A2 - Kinshuk, null
A2 - Sampson, Demetrios G
A2 - Chen, Nian-Shing
A2 - Chang, Maiga
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017
Y2 - 3 July 2017 through 7 July 2017
ER -