Teachers’ Technology Leadership: An Assessment of Postgraduate Pre-service Teachers’ Perceptions based on NLP-augmented Qualitative Analysis

Abstract

Qualitative analysis has been used to assess teachers’ technology leadership practice. However, it is not easy for qualitative researchers to reduce biases in the big data era, with issues such as big qualitative data and the complexity of data analysis. To achieve a more efficient and effective research, Natural language processing (NLP) can be a potential approach to address these methodological challenges. Since there was no study integrating qualitative analysis with NLP in this area, this study utilized an NLP-augmented qualitative analysis, which begins with qualitative data analysis then followed by NLP analysis in the design, for the assessment of teachers’ technology leadership practice. 133 postgraduate pre-service teachers in a Chinese normal university were selected to respond the open-ended question. Qualitative analysis was first conducted based on the ISTE-E standards, through categorizing codes into learners, leaders, citizens, collaborators, designers, facilitators, and analysts. NLP analysis, based on text preprocessing (including tokenization, part-of-speech tagging, and removing stop-words), feature extraction with the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and sentence selection and assembly, was utilized to triangulate the qualitative results. The NLP-augmented qualitative analysis results revealed that teacher educators can practice technology leadership based on the ISTE-E standards to support students’ technology integration.