Lee, Alwyn Vwen Yen

The widespread adoption of personal computers and mobile devices has enabled learning analytics to become more pervasive among teachers, school administrators, students and parents. While the past decade has marked notable advancements in learning analytics, less attention has been paid to the unique characteristics of learning analytics that necessitate the notion of learning analytics literacy. Although researchers have documented the common use and misuse of learning analytics in education, there is still limited research that highlights the importance of cultivating literacy around the use of learning analytics for better understanding of teaching and learning practices. This paper describes how a Micro-Meso-Macro (3M) analytical approach can be used to support and enhance learning analytics literacy among education stakeholders, while raising the prospects of how a systematic implementation of raising learning analytics literacy can be done through two interacting themes: raising awareness and raising criticality.

Teacher noticing is specialized to its purpose of noticing events and students that are central to the teachers’ professional goals. This study extended prior work of teacher noticing beyond case studies of individual classrooms into implementation across several schools, leveraging eye-tracking and video technologies to collect and analyze teacher-noticing patterns that complement video-based reflective dialogues for additional insights. Practices at the micro level (single school) were reconsidered and implemented at a higher meso level (across multiple schools) in this study. The findings show that differences between teachers’ noticing patterns across schools may be attributed to school cultures, teaching strategies, and teacher beliefs, backed by eye-tracking data analyses and reflective dialogues.

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 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.

The understanding of online classroom talk is a challenge even with current technological advancements. To determine the quality of ideas in classroom talk for individual and groups of students, a new approach such as precision education will be needed to integrate learning analytics and machine learning techniques to improve the quality of teaching and cater interventive practices for individuals based on best available evidence. This paper presents a study of 20 secondary school students engaged in asynchronous online discourse over a period of two weeks. The online discourse was recorded and classroom talk was coded before undergoing social network analysis and k-means clustering to identify three types of ideas (promising, potential, and trivial). The quality and distribution of ideas were then mapped to the different kinds of talk that were coded from the online discourse. Idea Progress Reports were designed and trialed to present collective and individual student’s idea trajectories during discourse. Findings show that the majority of ideas in exploratory talk are promising to the students, while ideas in cumulative and disputational talks are less promising or trivial. Feedback on the design of the Idea Progress Reports was collected with suggestions for it to be more informative and insightful for individual student. Overall, this research has shown that classroom talk can be associated with the quality of ideas using a quantitative approach and teachers can be adequately informed about collective and individual ideas in classroom talks to provide timely interventions.