A Machine-Learning Approach to Recognizing Teaching Beliefs in Narrative Stories of Outstanding Professors

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 beliefs of outstanding professors. In this study, the text information used for coding was a series of fourteen books published across fourteen years, namely The Stories of Outstanding Professors in National Taiwan University (NTU), which contained one million words describing the stories of three hundred NTU outstanding professors. Towards identifying six categories and thirty subcategories of teaching beliefs revealed in the narrative stories, our approach outperforms comparative methods by 1% to 86% in the F1 score. Comprehensive evaluations validate the effectiveness of our approach in assisting in not only recognizing the teaching beliefs from stories of outstanding professors, but also the process of coding text information from narrative stories.