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Lee, Alwyn Vwen Yen
- PublicationOpen AccessMultimodal learning analytics for knowledge building(National Institute of Education, Nanyang Technological University (NIE NTU), Singapore, 2024)
; ; ; ; ; 61 465 - PublicationEmbargoBreaking the silence: Understanding teachers’ use of silence in classrooms
Silence in classrooms is an undervalued and understudied phenomenon. There is limited research on how teachers behave and think during teachers’ silence in lessons. There are also methodological constraints due to the lack of teacher’s talk during silence. This study used eye-tracking technology to visualize the noticing patterns of two science teachers during silence lasting more than three seconds. Using video data recorded from cameras and eye trackers, we examined each silent event and interpreted teachers’ perceptions and interpretations with consideration of eye fixations, actions of students and teachers during the silence, and teachers’ actions immediately after they broke the silence. We further examined expert-novice differences in teachers’ use of silence. Four categories of teachers’ silence were identified: silence for (1) preparing the classroom for learning; (2) teaching, questioning, and facilitating learning; (3) reflecting and thinking, and (4) behavioural management. Expert-novice differences were identified, especially in the teachers’ use of silence for approaches to teaching, reflection, and behavioural management. The novel contribution of this paper lies in the characterization of silences as observed in actual classroom settings as well as the methodological innovation in using eye trackers and video to overcome the constraints of lack of talk data during silence.
85 5 - PublicationOpen AccessArtificial intelligence in education (AIED)(2020)The use of Artificial Intelligence (AI) in education is no longer science fiction but becoming a reality in these unprecedented times of dynamic changes. This field encompasses a wide range of techniques, algorithms, and solutions that may resolve current predicaments and problems in today’s classroom. This paper discusses how AI that is supporting the existing world can be extended into the fields of education and addresses the existing challenges of using AI within classrooms across Singapore.
485 363 - PublicationMetadata onlyInfrastructuring for knowledge building: Advancing a framework for sustained innovation(Collaborative Knowledge Building Group, 2023)
; ; WOS© Citations 3 55 - PublicationOpen AccessExploring students' epistemic emotions in knowledge building using multimodal data(2022)
; 178 226 - PublicationRestrictedIdea identification and analysis (I²A) for sustained idea improvement in knowledge building discourse(2019)Instructors adopting a dialogic approach to teaching and learning (Reznitskaya & Gregory, 2013) tend to design learning environments where there is shared control over classroom talks for collaborative meaning making and dialogic inquiry among learners. In this thesis, the dialogic pedagogical approach follows Scardamalia and Bereiter’s model of knowledge building (2003), which leverages a learner’s natural curiosity in questioning and inquiry. In such an environment, learners engage in social collaborative inquiries to contribute and advance communal knowledge. However, working to improve one’s ideas requires considerable support (Scardamalia & Bereiter, 2014). For example, during the initial phase of the inquiry, learners could raise many competing ideas. Recognising and identifying promising ideas is an approach to support such processes and is also an important component of expertise and creativity (Bereiter & Scardamalia, 1993; Gardner 1994). Technological tools, such as online forums (e.g., Knowledge Forum), can provide conducive platforms to the sharing of ideas, but learners still encounter potential difficulties in identifying promising ideas that would be relevant, communally interesting, and of impact to the community. The goal of this research is to identify and analyse promising ideas that can sustain idea improvement in knowledge building discourse, and investigate the effect of these ideas on the collective advancement of communal knowledge. This thesis encapsulates the development and implementation of a methodology called Idea Identification and Analysis (I2A), which uses network analysis, temporal analytics, and machine learning techniques to define the attributes of promising ideas, recognise various idea types, and determine the effect of such ideas within online knowledge building discourse. The methodology is guided by an Idea Pipeline framework that is modelled after a classic innovation process. Two studies were conducted in this thesis to search for promising ideas and to determine the feasibility and scalability of the I2A methodology. The first study was exploratory in nature and its results determined the attributes of idea promisingness using the betweenness centrality (BC) network measure. A classification system was developed to determine idea types in discourse based on the recognition of BC trends. A temporal analysis was applied to identify promising ideas in discourse and the findings were qualitatively validated. Building upon the first study, the second study further improved the I2A methodology using text mining, new visualisation, and clustering techniques. In addition to confirming the presence and nature of promising ideas from the first study, learners’ input in discourse were examined to determine the mobility of ideas in discourse and various idea types could be automatically recognised. These processes also helped to confirm and uncover new promising ideas in discourse. In essence, this thesis contributes to the field of Computer-Supported Collaborative Learning (CSCL) with the development of a framework and methodology that will guide and assist future research and development in closing the knowledge gap in idea analysis within knowledge building discourse.
429 47 - PublicationOpen AccessDiscovering dynamics of an idea pipeline: Understanding idea development within a knowledge building discourse(2017)
; Idea development is an important process within a knowledge-building discourse and it is crucial to understand the dynamics of idea development throughout the discourse, such as the growth, flourishing or fading of ideas. This study proposes a framework called Idea Pipeline that explores and tracks the dynamics of idea development within a knowledge-building discourse. This pipeline consists of three phases: discovery, identification and analysis, and ‘rise above’ of ideas. Each phase of the pipeline will be illustrated using findings from a comparison study of two online knowledge building discourses. During the discovery phase, a text miner is used to identify groups of related keywords from the discourse; this is represented as keyword graphs with weighted frequencies to show the diversity of ideas that were embedded within the knowledge-building discourse. In the idea identification and analysis phase, network analysis was conducted to help label key ideas that were promising to the discourse community; this would provide the community with information to decide which ideas to pursue so that advancement of communal knowledge could be achieved leading to the ‘rise above’ phase. This Idea Pipeline framework can be an additional method for the temporal analysis of a computer-supported collaborative learning discourse over a longer duration of weeks or even months.470 405 - PublicationMetadata onlyDetecting patterns of idea novelty and complexity in student knowledge building discourses(International Society of the Learning Sciences, 2024)
; ; ; Ong, Aloysius Kian-KeongThis 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.51 - PublicationOpen AccessAI in education and learning analytics in Singapore: An overview of key projects and initiatives(Japan Society for Educational Technology & Japanese Society for Information and Systems in Education, 2023)
; ; 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.35 105 - PublicationOpen AccessPrompt 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.
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