Towards recognition of students' epistemic emotions in a student knowledge building design studio
When students build knowledge and apply critical thinking to real ideas and problems around them, their expressed emotions are important to recognize as drivers of knowledge acquisition of themselves and the world. These epistemic emotions are also critical for knowledge generation and cognitive performance. In this pilot study, we attempt to examine and recognize students’ epistemic emotions in an informal learning environment, student Knowledge Building Design Studio (sKBDS), that was designed for cultivating collaborative knowledge creation and enhancement of student agency. A sensing module was developed to collect students’ facial and Heart Rate Variability (HRV) data, before using machine learning algorithms and models that are trained on students’ data to recognize the different types of epistemic emotions that students exhibit during an empirical knowledge building study. Initial findings are promising and shows the possibility of recognizing students’ epistemic emotions in real-time.
Exploring students' epistemic emotions in knowledge building using multimodal data
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.
Rethinking teaching and learning with preschoolers: Professional development using knowledge building and a 3M analytical framework
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.
A step toward characterizing student collaboration in online knowledge building environments with machine learning
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.
Epistemic network analysis to assess collaborative engagement in knowledge building discourse
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.
Connecting teachers during a global crisis: A knowledge building professional development approach to embracing the new normal
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.
Detecting patterns of idea novelty and complexity in student knowledge building discourses
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.
Prompt engineering for knowledge creation: Using chain-of-thought to support students’ improvable ideas
Previous research on providing feedback on public speaking has investigated the effectiveness of feedback sources, namely teacher feedback, peer feedback, and self-feedback, in enhancing public speaking competence, predominantly individually. However, how these sources of feedback can be collectively harnessed to optimize learner engagement and public speaking performance still warrants further investigation. Adopting a pre- and post-test quasi-experimental design, this study randomly assigned four classes to four feedback conditions: Group 1 received teacher feedback, Group 2 self-feedback and teacher feedback, Group 3 peer and teacher feedback, and Group 4 feedback from all three sources. Both student engagement, measured using the Public Speaking Feedback Engagement Scale (PSFES), and their public speaking performance ratings, assessed using the Public Speaking Competency Instrument (PSCI), were validated using Rasch analysis. The inferential statistics revealed that Group 3 showed significant improvements across nearly all three dimensions of engagement, whereas Group 2 experienced significant declines in all dimensions of engagement except behavioral engagement. Group 3 demonstrated significantly greater engagement gain compared to Groups 2 and 4, indicating the synergistic effect of peer and teacher feedback in contrast to the limited impact of self-feedback. Additionally, all groups demonstrated significant improvements except for Group 2, which showed significantly lower improvement compared to Group 4. The following correlation analysis identified a significant correlation between the gain of students’ behavioral engagement and the gain of public speaking performance, whereas such association was absent between cognitive or emotional engagement and public speaking competence. This study suggests that peer feedback should be preceded by group discussion and supplemented with teacher feedback in classes for enhancing the teacher–student dialog, while self-feedback should be conducted after class to improve student engagement and public speaking performance.
Implementing learning analytics interventions to support student agency in knowledge building
Research and development of Learning Analytics (LA) have created new ways to support students’ learning. However, our understanding of teachers’ roles when implementing LA in classroom practices remains nascent. This study investigates how teachers can implement LA to support students’ agency in directing their own inquiry, when engaging in a digital pedagogy approach known as Knowledge Building (KB) on an online platform called Knowledge Forum (KF). Together with a teacher experienced in KB and KF, we co-constructed interventions anchored on two easy-to-use LA tools in KF, the Scaffold Tracker and Word Cloud. These LA interventions were enacted with a Grade 5 class of 18 students and another Grade 6 class of 18 students. Three teacher roles were identified: detecting behaviour unproductive to collaboration, mediating between LA and student self-assessment, and framing student-generated lines of inquiry in actionable ways. We coded KF notes for the discourse moves they reflected and found that after the teacher implemented the LA interventions, notes that sustained inquiry increased sharply with students’ focus shifting from individual to collective knowledge and from superficial explanations to deep understandings. We discuss the implications of these findings on the development of ways to bridge LA research and practice.