Collaborative Science Inquiry (CSI) is a pedagogical approach that fosters students’ scientific skills and competencies through group-based inquiry activities. However, CSI poses various challenges for teachers (i.e., recognizing the problems and needs of each group, identifying appropriate strategies to address them, and reviewing the impact of these strategies) to support students’ learning process across different contexts and social levels, especially in a Mobile Learning Environment (MLE). To address this problem, this study adopted Design-Based Research (DBR), aiming to design, develop, implement, and evaluate a theory-led LA tool on a mobile app, namely, the m-Orchestrate app to support teacher orchestration in CSI. The tool aligns with the inquiry cycle model and provides teachers with real time data and feedback on students’ inquiry progress, collaboration, and learning outcomes. The DBR lasted three years, involving four phases, including (1) Phase I: Analysis of practical problems in orchestrating CSI in an MLE, (2) Phase II: Design and development of a theory-led LA tool, (3) Phase III: Three iterative cycles of testing and refining the LA tool in practice, and (4) Phase IV: Reflection and further implementation of the LA tool. The researcher collaborated with two primary schools, four teachers, two pre-service teachers in General Studies, and around 250 students in Grade 4 in Hong Kong. The research questions focused on (1) investigating the affordances of the LA tool with theory-led and interactive design for teacher orchestration of CSI; (2) the impact of the tool on teacher orchestration in CSI; and (3) the impact of teacher orchestration supported by the tool on student performance in CSI. Data collection included log data from the LA tool and the m-Orchestrate app, teacher interviews, and students’ pre- and post-quizzes. Both qualitative and quantitative data analysis methods were adopted. The research findings show that (1) the affordances of the developed LA tool include interactive features with just-in-time orchestration, Business Process Analytics in R (bupaR) (Janssenswillen et al., 2019) features to visualize students’ CSI process in real time, and Python implementation of the Bayesian Knowledge Tracing (pyBKT) (Anirudhan et al., 2021) features to identify highly-related behaviors to phase completion of CSI learning from previous projects; (2) the LA tool could help teacher orchestrate CSI effectively; and (3) teacher orchestration using the LA tool could help enhance student performance in CSI. The LA features of the LA tool provided a deeper understanding of students’ CSI learning processes for evidence-based orchestration practice. The significance of the study lies in three aspects. First, theoretically, the theory-led LA tool, a process model of the LA-enhanced orchestration, and design principles contribute to LA-enhanced teacher orchestration CSI in an MLE. Secondly, technically, the technical design and development of the theory-led LA tool with the three affordances for teacher orchestration in CSI in an MLE can shed light on future LA tool development. Thirdly, practically, the approach of integrating the LA tool into teaching and learning in CSI in an MLE could effectively enhance teacher orchestration and students’ performance, an area rarely explored in the literature. Limitations and future work are also discussed for further design, development, and implementation of LA-enhanced teacher orchestration of CSI in an MLE. All rights reserved.
|Qualification||Doctor of Philosophy|
|Publication status||Published - 2023|
- Teacher orchestration
- Collaborative Science Inquiry (CSI)
- Mobile Learning Environments (MLEs)
- Theory-led design
- Learning Analytics (LA)
- Theses and Dissertations
- Thesis (Ph.D.)--The Education University of Hong Kong, 2023.