Teo, Chew Lee

Existing research has substantial progress in uncovering outcomes of collaborative learning in recent years, but more attention can be directed towards the better understanding of collaborative learning processes via quantitative frameworks and methods. Through the use of knowledge building as a collaborative learning pedagogical approach, it is possible for researchers to glean deeper insights into aspects of students’ collaboration within authentic learning environments. In this paper, the multimodal approach of data collection and analysis was conducted with a proposed conceptual analytical framework that can characterize constructs of collaborative activities in a knowledge building classroom using machine learning methods. The application in a pilot is discussed along with how this conceptual development can offer a summary of new insights into students’ individual and group collaborative trajectories during learning tasks.

Grasping students' emotions, especially those relating to learning, in a collaborative setting is no easy feat for teachers. The quality of collaboration comprises both visible behavior and emotion and the less visible emotional traits relating to engagement and motivation. Teachers often rely on their experience and intuition when it comes to these invisible traits. In this study, we collected multimodal data from a collaborative knowledge building classroom to analyze when and how students' emotions transpire during the working and improvement of ideas. Data included textual data, self-reports from surveys, interviews, and physiological data from face-to-face and online knowledge building discourse of 17 students in a 2.5-hour Social Studies lesson. We found shifts in epistemic emotions during idea improvement activities, and the students explained these shifts in understanding the discussion and engaging in idea-centric processes. We discuss findings for ongoing work to develop multimodal analytics for knowledge building practice.

The curriculum document remains as one of the most important resource and reference for teachers. Teachers typically follow the prescribed learning objectives in the curriculum for a particular grade level, there is no need for them to take students beyond the grade level. This approach creates a clear baseline knowledge, but it limits the opportunities for further exploration. Thus, it is not surprising that curriculum syllabus is often regarded as a constraint to student inquiry and discourse.

This case study explores how a science teacher adopted knowledge building and learning analytics to support a class of primary five students to collaboratively inquire and learn about electricity. Specifically, we aim to understand how the teacher implemented a lesson design guided by knowledge building principles of idea improvement and community knowledge and how he used visualisations from an analytics tool to facilitate students in collaborative inquiry in science. We collected student notes from their online discourse in Knowledge Forum, video-recorded a total of 11 lesson videos and conducted interviews with the teacher and students. We found that students’ online discussion reflected explanation-seeking questions to sustain the inquiry on the topic and explanations to deepen and improve their ideas on concepts of electricity. We also found that the visualisations from our analytics tool supported (i) teacher-facilitated whole-class discussions on curriculum keywords and student ideas to develop conceptual understanding and idea-building, and (ii) students in exploring science ideas they were interested in. The findings from our study contribute to the understanding of teachers’ enactment of inquiry-supported pedagogies in primary science classrooms.