TY - GEN
T1 - Automatic feedback models to students freely written comments
AU - Makhlouf, Jihed
AU - Mine, Tsunernori
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grant Numbers JP18K18656, JP19KK0257, JP20H04300, and JP20H01728.
Publisher Copyright:
© ICCE 2020 - 28th International Conference on Computers in Education, Proceedings. All rights reserved.
PY - 2020/11/23
Y1 - 2020/11/23
N2 - Teachers and professors, in different educational institutions, always wanted to grasp the learning experience of their students so they can give them proper guidance and intervene when it is necessary. However, tracking every student can be very challenging. In this context, we created an online questionnaire and asked students to fill it after each lesson. The Professor read what the students had written to get a better understanding of their learning experience and to reply to them with the adequate guidance. However, it turns out that professors quickly become overwhelmed and students have to wait longer before receiving any feedback. In this paper, we describe our method of building an automatic feedback model that will be used to help professors. We tried two approaches of building the models with or without a padding of the context. Empirical results show that our padded models can achieve 0.664 micro F-score.
AB - Teachers and professors, in different educational institutions, always wanted to grasp the learning experience of their students so they can give them proper guidance and intervene when it is necessary. However, tracking every student can be very challenging. In this context, we created an online questionnaire and asked students to fill it after each lesson. The Professor read what the students had written to get a better understanding of their learning experience and to reply to them with the adequate guidance. However, it turns out that professors quickly become overwhelmed and students have to wait longer before receiving any feedback. In this paper, we describe our method of building an automatic feedback model that will be used to help professors. We tried two approaches of building the models with or without a padding of the context. Empirical results show that our padded models can achieve 0.664 micro F-score.
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M3 - Conference contribution
AN - SCOPUS:85099467050
T3 - ICCE 2020 - 28th International Conference on Computers in Education, Proceedings
SP - 336
EP - 341
BT - ICCE 2020 - 28th International Conference on Computers in Education, Proceedings
A2 - So, Hyo-Jeong
A2 - Rodrigo, Ma. Mercedes
A2 - Mason, Jon
A2 - Mitrovic, Antonija
A2 - Bodemer, Daniel
A2 - Chen, Weichao
A2 - Chen, Zhi-Hong
A2 - Flanagan, Brendan
A2 - Jansen, Marc
A2 - Nkambou, Roger
A2 - Wu, Longkai
PB - Asia-Pacific Society for Computers in Education
T2 - 28th International Conference on Computers in Education, ICCE 2020
Y2 - 23 November 2020 through 27 November 2020
ER -