This study explored the demographic predictors of teachers’ stages of concern (SoC) for science, technology, engineering, and mathematics (STEM) education in Hong Kong. Results of multiple regression analysis demonstrated that teaching sector and participation in STEM education training predicted four SoC, whereas teaching subject area predicted two SoC. We discussed the significance of these findings by adopting a personalized approach to teacher professional learning. Copyright © ICCE 2020 - 28th International Conference on Computers in Education, Proceedings. All rights reserved.

In this paper, we explored the primary students' after-class vocabulary behavioural learning patterns using a mobile user-generated-content (m-UGC) tool. A total number of 21 Grade 4 students from an elementary school in Hong Kong were recruited. Case study approach was adopted. Data collection included students' learning logs on the m-UGC tool. Content analysis and visualization were used to analyze data. Three sub-groups of primary students' after-class vocabulary behavioural learning patterns were identified and future studies were explored. Copyright © ICCE 2020 - 28th International Conference on Computers in Education, Proceedings. All rights reserved.