Lee, Alwyn Vwen Yen

The theory and practice of knowledge building is one of the most well-known and influential educational approaches seeking to foster a culture of creativity, collaboration, and innovation in classrooms. To date, knowledge building research has focused on the sociocognitive and technological dynamics required to sustain knowledge building but has not focused specifically on the unique infrastructures that enable them. To contribute new ideas that can advance our understanding of what and how infrastructures enable knowledge building, this workshop will explore and synthesize ongoing research that includes a variety of examples that deal with and conceptualize knowledge building infrastructures. The workshop is organized into four sections, with both pre- and post-activities, and have three types of participants: invited speakers; key contributors; and active participants. Ultimately, the goal is to find ways to widen participation in knowledge building endeavors within existing or new implementations across the world.

This paper presents a study of temporal analytics and discourse analysis of an online discussion, through investigation of a group of 13 in-service teachers and 2 instructors. A discussion forum consisting of 281 posts on an online collaborative learning environment was investigated. A text-mining tool was used to discover keywords from the discourse, and through social network analysis based on these keywords, a significant presence of relevant and promising ideas within discourse was revealed. However, uncovering the key ideas alone is insufficient to clearly explain students’ level of understanding regarding the discussed topics. A more thorough analysis was thus performed by using temporal analytics with step-wise discourse analysis to trace the ideas and determine their impact on communal discourse. The results indicated that most ideas within the discourse could be traced to the origin of a set of improvable ideas, which impacted and also increased the community’s level of interest in sharing and discussing ideas through discourse.

Unprecedented global issues have caused large-scale disruption to preschools, requiring a rethink of existing teaching and learning approaches. This study examines the inception of a knowledge building approach in preschools using a case study method and a micro‑meso-macro (3M) analytical framework to understand impact and implications. We found that teachers and students, as micro units (individual agents) collaborated with each other at the meso level, where knowledge creation is carried out within communities, while interacting with macro units (school leaders). Analysis shows the knowledge building approach aided stakeholders in navigating organizational, technological, and pedagogical challenges. Elevating awareness and acknowledging impacts of the knowledge building approach can inspire critical discourse to understand and handle future disruptions in the educational landscape.

Artificial Intelligence (AI) in education and learning analytics (LA) tools are increasingly being developed and implemented to enhance teaching and learning within Singapore’s education landscape. This paper provides an overview of key AI in education and LA projects and initiatives in Singapore, organized by the types of technology. The identified projects and initiatives involve a range of techniques and systems to achieve personalized learning, improve student engagement, optimize resources, and also predict student success among a list of educational outcomes. We briefly describe each identified project before further discussing the collective impact and limitations, as well as the implications for Singapore and her education environments. Overall, this paper seeks to provide an overview of the state and use of AI and LA in education-related projects within Singapore and highlights the need for further research and development in this area to fully realize the potential of these technologies for improvement of teaching and learning.

This paper investigated the differences between the online discourses of K12 and higher education students, in an effort to understand differences in knowledge building behaviors between them. The higher education students are represented by 13 in-service graduate teachers and K12 students include 20 eighth-graders. An idea-centric analysis was conducted, which showed that discourse in the K12 setting contained ideas less promising to the discourse community than those found in higher education discourse. By considering the differences in the build-on of notes and utilization of scaffolds in discourse, potential strategies were suggested to change the patterns of build-on and use of scaffolds in Knowledge Forum so as to improve the quality of ideas in knowledge building discourse. Teachers can focus resources on promising ideas and strategize the use of scaffolds to maintain engagement and idea improvement within knowledge building discourse for better understanding and attainment of knowledge building goals.

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.

The assessment and understanding of students’ ideas in discourse have often been a difficult problem for teachers to tackle. Recent innovations and technologies such as text mining can provide a partial solution by generating an estimated count of important keywords which are representative of ideas within discourse. However, investigating idea development and flow within discourse is a much more challenging task, and requires elaborate processing and analysis. In this study, a method for analysing idea flow was proposed and tested: (a) text mining and network analysis are employed to identify and validate ideas from textual discourse; (b) identified ideas are grouped together and mapped to relevant learning objectives; (c) groups of ideas are then aggregated using a binning method and scored; (d) a flow diagram is generated using the aggregated scores to visualize idea flow within discourse. By understanding how ideas flow within discourse, discussed key ideas can be monitored and any lapses in student understanding can be identified so that teachers will have information to provide timely interventions to support and scaffold learning.

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.

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.