This study examined the role of preservice teachers' (PSTs') teaching self-efficacy in a training program, which focused on integrating computational thinking (CT) in elementary subjects. Results showed that PSTs' CT knowledge significantly predicted their teaching self-efficacy though the explanatory power was weak. Evidence from the qualitative analysis on PSTs' post-lesson reflections showed different patterns between PSTs with positive and non-positive teaching self-efficacy. These results not only provided insights into future research but also highlighted the need to develop PSTs' pedagogical skills alongside their CT knowledge during the training.

Recent years have seen developments in AI instructional practices for K-12 students. In literature, students' interest in AI is shown to correlate with gaining AI knowledge; however, little is known about how AI interest manifests in classroom discourses during AI literacy lessons. This study examined students' participation in an integrated AI curriculum delivered to a cognitive science class in a high school in the southern US. Students worked in small groups and built a supervised machine learning model to recognize kids' drawings at different stages of artistic development. Our analysis showed that semantic features extracted from students' small group conversations significantly predicted their interest in learning AI. However, we found no significant relationship between students' social construction of knowledge and their interests. This study sheds light on the relationship between the learning process and interest; when further developed, this analysis may be developed into a classroom activity analytics tool that may provide real-time feedback to teachers engaged in AI literacy education to enhance teaching effectiveness in this nascent content area.

With the increased popularity of mobile learning, there is a growing demand on the understanding of how the scalable technology, such as mobile devices, influences individual learning behaviours as well as their learning outcome. A theoretical model was built based on the adaptive structuration theory (AST) and the knowledge spiral theory. Using this model, we examined the relationship between structural sources, individuals’ adaptive structural behaviours, and their perceived learning. A Structural Equation Modeling method was employed in our empirical study. Findings indicate that users’ task adaptation had a positive influence on their perceived learning, In addition, their exploitive technology adaptation influenced the ultimate perceived learning, but the impact of users’ exploratory technology adaptation on learning was mediated by their task adaptation. Contrary to expectations, the effect of computer self-efficacy on exploitive and exploratory technology adaptation was negative, and exploratory technology adaptation negatively affected exploitive task adaptation. A detailed discussion of the findings and implications are provided in this paper.