Teo, Chew Lee

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.

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.

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.

This paper explores the application of a framework for idea novelty in students’ discourse for knowledge building. Knowledge building promotes collaborative discourse among students and supports them in expanding collective community knowledge. However, students often go beyond the sharing of information, and they contribute novel ideas that are vital to deepening community knowledge and expanding collective inquiry. Novel ideas not only reveal the character and quality of the discourse but also show how the conversation may extend to deepen the understanding of a challenging topic. This study attempts to illuminate novel ideas from students as they engage in knowledge building using the analytical lens of novelty. Data analysis from exploratory analysis and multiple correspondence analysis revealed patterns of how students contribute novel ideas to sustain their conversation. Utilizing advanced Machine Learning techniques, this study effectively identified and quantified patterns of idea novelty and complexity in student discourses, enhancing the understanding of collaborative knowledge construction.

A global crisis such as the COVID-19 pandemic has disrupted almost every industry and the field of education is also affected with safe distancing measures and minimal face-to-face interactions between teachers, students, and their families. However, new opportunities and technologies have emerged for teachers to utilize and work with students and their parents. We investigated a case study of a community of pre-school teachers who continued their professional discussions on a virtual and asynchronous discussion platform throughout the lockdown period caused by COVID-19. The teacher community planned for and conducted lessons using the knowledge building approach. This paper reports the considerations and implementation of a community-based professional effort through times of immense disruptions and have shown evidence that the knowledge building approach can propel a community of learners to construct collective inquiries and solutions to deal with emerging problems through the lockdown period. The knowledge building approach can potentially enculturate teachers towards noticing new and emergent ideas in their classes and thereby elevating the awareness of teachers to design and build new knowledge of their practice. Such teachers' professional culture is conducive for tackling the constant change and disruption in the educational landscape, such as the one brought about by the COVID-19 pandemic.

Knowledge Building (KB) is an established learning sciences theory that seeks to promote innovative ideas and idea improvement among students via collaborative engagement in productive discourse. KB discourse supports students to make constructive discourse moves such as questioning, explaining with evidence, adding new information and so on, to advance the collective inquiry. However, current understanding on KB discourse remains limited to students’ online participation. Although small group discussion is a common practice, there is little understanding on the role of verbal discussions to support KB discourse. This paper attempts to address this line of inquiry by assessing student engagement in KB discourse supported by both online and verbal discussions. Data is retrieved from a group of six students in a Grade 6 Social Studies class. The group participated in a 2.5hr lesson designed with opportunities for discussions on the Knowledge Forum (online) and in small groups (verbal). Group talk was transcribed, and Knowledge Forum notes were coded for its semantic level of contribution, with the codes being analysed for weighted connections using Epistemic Network Analysis (ENA). The ENA analysis revealed clear differences in both group and individual engagement between the online and verbal discourse. Notably, students’ contributions on Knowledge Forum showed an apparent pattern of stronger connections among codes of higher semantic levels, suggesting that students were more cognitively engaged in the online discussion than their group verbal talk. Implications for research and practice are discussed.

In this paper, we present the design of a two-day student programme called the student Knowledge Building Design Studio (sKBDS), intended to promote collective cognitive responsibility (CCR) by focusing on student interests in real sustainability-related problems and giving them opportunities to drive the collective inquiry. Participants included 36 primary students from three different schools (six interest groups). The design of sKBDS shows how CCR developed over time across interest groups. Our analytical approach included the use of theory building moves to code students’ ideas and the use of an analytics tool called “Ideas-Building” to examine collaborative patterns from their online discussions. Our findings suggest a positive impact of the sKBDS design in supporting students to theorize, build, and improve ideas around their sustainability-related problem. However, we also found salient patterns in collaborative engagement across groups, suggesting that CCR development is non-linear with purposeful student activities. We then discuss the implications for CCR designs in practice.

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.