Abstract

The coding of text information to recognize the teaching beliefs of outstanding professors is crucial research to enhance teaching performance in university. Most previous studies adopted manual coding, and thus text information was limited to briefly descriptive statements or questionnaires, rather than full narrative stories of outstanding professors, owing to the time-consuming of manual coding. However, outstanding professors’ narrative stories, which contained more detailed information about the outstanding professors’ thinking and behaviors, were valuable text information to recognize the types of teaching beliefs of outstanding professors. Therefore, to overcome the time-consuming obstacle of manual coding, this study proposes a machine-learning-based approach, which exploits BERT with convolutional LSTM, to code narrative stories of outstanding teachers for the identification of the types of teaching …

Date

June 26, 2023

Authors

Fandel Lin, Ding-Ying Guo, Jer-Yann Lin

Book

International Conference on Artificial Intelligence in Education

Pages

739-745

Publisher

Springer Nature Switzerland