Teo, Chew Lee

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.

Cultivating students' data science knowledge and skills is pressing and challenging, given its interdisciplinary nature, students' limited prior knowledge, and teachers' insufficient training. In data science learning, students may experience various academic emotions. Understanding what emotions students experience, how these emotions are associated with their perceived learning, and under what conditions they experience intensive emotions is critical to informing the design of data science programs and better supporting students. This study collected 839 emotion survey responses from 67 secondary school students in two cycles of a two-day out-of-school data science program. The program engaged students in collaborative inquiries on authentic problems through data science practices with the support of teachers, researchers and facilitators. We found that frustration, interest, surprise and happiness positively predicted students' perceived learning, whereas anxiety negatively predicted perceived learning. Students experienced peaks of positive emotions after an expert's enthusiastic introduction talk to data science in the first cycle and after one-to-one face-to-face consultations with data science experts in the second cycle. However, sharing their progress and challenges with the data science expert in the first cycle and preparing for presentations in both cycles made them experience intense negative emotions such as anxiety, frustration, and confusion. These findings provide implications for designing data science programs to elicit students' positive learning experiences and reduce intensive negative emotions.

Student-Generated Ideas (SGI) is a socio-constructivist pedagogical approach that positions the development of student-generated ideas at the core of the learning trajectory alongside desired learning outcomes. This paper investigates the use of whole-class discussion as a pedagogical strategy to foster the evolution of SGIs in a blended primary science lesson in the Singapore context. Through a case study, we examine the design and implementation of a technology-enhanced SGI lesson, focusing on how students' ideas were generated and evolved, and the discursive practices employed by both the teacher and the students. Through qualitative data analysis, our findings uncover the richness of the SGIs, the teacher’s adept orchestration of the discussion, the diverse cognitive processes exhibited by the students and the scaffolds employed by the teacher. The discussion extracts key design and enactment implications, which encompass, but are not limited to, the integral role of technology in supporting idea evolution beyond the lesson and the important role of the teacher in the effective use of whole-class discussion in SGI lessons.

To deepen and sustain an innovative practice in a school, each layer of players within the organization—students, teachers, teacher-leads, head of department, school leaders, play different roles in contributing and advancing the vision and practice of the innovation. Not only that, the way these ‘mid-layer leadership’ interacts to create a coherent force in moving the innovation culture is critical. In this study, we look particularly at the role of middle managers in deepening and sustaining a twenty-first century teaching and learning practice and knowledge building within the ecosystem of the whole school. We look at this practice as it did not particularly receive top-down or bottom-up support at the on-set of the project in the case studies below. The decision to embrace and experiment with the practice was taken by the middle manager and much of the navigation, strategizing and advancing within the organization relied on these middle managers as well. In this chapter, we analyse the work of three middle managers to understand the realities of leading from the middle through identifying key dimensions, strategies and approaches adopted as well as the tensions they experienced as ‘mid-layer leaders’ in sustaining knowledge building practice and culture in their school.

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 Learning Sciences investigate all aspects of learning, key to which is a consideration of all ecological factors and establishing the ecological validity of research in authentic settings. Educational Neuroscience has surfaced micro-level evidence to the various constructs explored in the Learning Sciences but with quite distinct methodologies and interpretations. The rapid development in both Learning Science and Neuroscience research presents new opportunities to amalgamate these two fields. This workshop invites researchers from both fields to discuss the potential and gaps in the integration of the research processes, the related theoretical and methodological perspectives of these two fields. We will discuss and demonstrate methods to measure cognitive, psychological, and neurological data in an authentic Knowledge Building classroom, mapping constructs and variables to integrate the intricate research processes across both fields.

This panel aims to facilitate an exchange between scholars specialized in various technology-enhanced socio-constructivist learning approaches with the common ground of placing Student-Generated Ideas (SGI) in the center of the learning trajectory. Despite varying theoretical underpinning and socio-cognitive mechanisms, these learning approaches similarly elicit ideas contributed by individual or groups of students in diverse forms and put them into the learners’ classroom or online community space to advance their learning. Examples of such approaches are not restricted to those which will be explicated by the panelists, namely, seamless learning, knowledge building, guided student questioning, student-generated questions, ubiquitous learning log, and productive failure-based flipped classrooms, but may also encompass problem-based learning, project- based learning, computational thinking, STEM, design thinking, makers, etc. The roles of technology in facilitating and enhancing such learning trajectories will be discussed.

Utilizing generative artificial intelligence, especially the more popularly used Generative Pre-trained Transformer (GPT) architecture, has made it possible to employ AI in ways that were previously not possible with conventional assessment and evaluation technologies for learning. As educational use cases and academic studies become increasingly prevalent, it is critical for education stakeholders to discuss design considerations and ideals that are key in supporting and augmenting learning via quality classroom discourse that sets the climate for student learning and thinking, and teachers’ transmission of expectations. In this paper, we seek to address how emergent technological advancements such as GPT, can be considered and utilized in designs that are consistent with the ideals of sustainable student discourse and knowledge creation. We showcase contemporary exemplars of possible designs and practices that are based on the pedagogy of knowledge building, with recent illustrations of how GPT may be utilized to sustain students’ knowledge building discourse. We also examine the potential effects and repercussions of technological utilization and misuse, along with insights into GPT’s role in supporting and enhancing knowledge building practices. We anticipate that the findings, through our exploration of designs and practices for knowledge creation, will be able to resonate with a broader audience and instigate meaningful change on issues of teaching and learning within smart learning environments.

Educational research may have established intricate connections between student achievements and emotions, but there remains a need to conduct more research on the crucial role of students’ epistemic emotions during learning. The emergence of global knowledge societies has nudged researchers to delve deeper into the understanding of students’ epistemic emotions within evolving learning environments, such as knowledge building environments that encourage complex learning and knowledge creation. This study addresses this gap via a naturalistic study of students' epistemic emotions in a student Knowledge Building Design Studio (sKBDS). We aim to illuminate the intersections between epistemic emotions and knowledge building activities, with findings to inform the design of more rigorous studies and designs to advance knowledge building practices. An Epistemic Emotion Survey (EES) was adapted for gathering students’ epistemic emotions and to align with knowledge building activities in the sKBDS. A total of 1,022 sets of epistemic emotion data from 73 primary and secondary school students were collected from two runs of the sKBDS, compiled into a single repository for descriptive analysis. Findings show that students experienced heightened curiosity, interest, excitement, and were generally happy to participate in activities at the sKBDS, while demonstrating relatively less anxiety, frustration, and confusion when undergoing knowledge building activities. Throughout the sKBDS, students also exhibited surprise at planned activities and what they have discovered and worked on. In addition, knowledge building activities also had varying effects on students' emotions, ranging from tiredness and hunger to occasional positive feelings. Overall, the findings from this study will be used for improving knowledge building practices and designs in future design studios, with implications for educators, students, and researchers.