Conference Papers

In prior literature on using clickstream data to capture student behavior in virtual learning environments, procrastination is typically measured by the extent to which students delay their coursework. However, students may delay coursework under personal and environmental contexts and not all delays should be considered procrastination. Thus, this study aims to identify different types of delayers and examine how they differ in academic engagement and performance. We utilized learning management system (LMS) data from three online undergraduate courses. Specifically, using data from the first three weeks of the course, we classified delayers into three subgroups – high-achieving, low-achieving, and sporadic delayers – based on the timing of their coursework access and submission, the consistency of these behaviors, and their short-term course performance. Our findings reveal that the subgroups significantly differ in course engagement and long-term performance. Low-achieving delayers exhibited the lowest levels of engagement and performance. While sporadic delayers and high-achieving delayers demonstrated comparable levels of engagement, the latter received higher course grades. These findings challenge commonly used LMS measures for procrastination, highlight the complexity of academic delays, and reveal nuanced patterns of student behavior. The results contribute to discussions on future interventions and research related to distinct forms of delays.

Pair programming is a collaborative activity that enhances students’ computational thinking (CT) skills. Analyzing students’ interactions during pair programming provides valuable insights into effective learning. However, interpreting classroom dialogues is a challenging and complex task. Due to the simultaneous interaction between interlocutors and other ambient noise in collaborative learning contexts, previous work heavily relied on manual transcription and coding, which is labor-intensive and time-consuming. Recent advancements in speech and language processing offer promising opportunities to automate and scale up dialogue analysis. Besides, previous work mainly focused on task-related interactions, with little attention to social interactions. To address these gaps, we conducted a four-week CT course with 26 fifth-grade primary school students. We recorded their discussions, transcribed them with speech processing models, and developed a coding scheme and applied LLMs for annotation. Our AI-driven pipeline effectively analyzed classroom recordings with high accuracy and efficiency. After identifying the dialogue patterns, we investigated the relationships between these patterns and CT performance. Four clusters of dialogue patterns have been identified: Inquiry, Constructive Collaboration, Disengagement, and Disputation. We observed that Inquiry and Constructive Collaboration patterns were positively related to students’ CT skills, while Disengagement and Disputation patterns were associated with lower CT performance. This study contributes to the understanding of how dialogue patterns relate to CT performance and provides implications for both research and educational practice in CT learning.

Since 2006, computational thinking (CT) has been popularised as a critical interdisciplinary skill and linked to mathematical thinking, solidifying its applicability in mathematics education. Singapore has actively introduced CT in its mathematics curriculum and provided professional development (PD) opportunities for mathematics teachers to develop their competencies in incorporating CT in mathematics classrooms. However, research examining how and whether such PD prepares educators to teach mathematics using CT is scarce. The study fills this gap by examining how a PD course that introduces the VBA coding language in Microsoft Excel for computational problem solving develops mathematics educators’ coding skills and CT, and how participating in-service teachers perceive the course with regards to learning coding. Qualitative analysis of the course design revealed that the course materials are capable of helping learners develop CT through instilling in them certain coding habits, while qualitative and quantitative analysis of Likertscale and open-ended responses in the course feedback highlighted many strengths and suggested areas for improvement in various aspects of the course, like course structure, course materials, level of course difficulty, and perceived usefulness and applicability of the course. These findings reveal the benefits of computational approaches adopted in this study for developing CT and coding skills, the relevance of such approaches in mathematics education, areas that can potentially be improved for more effective PD, as well as how rich insights generated by feedback and course design analysis can contribute to assessing the impact of PD and tailoring PD courses to specific teacher needs and concerns.

One of the aims of the current Advanced Level H2 Mathematics Syllabus in Singapore is for students to connect ideas within mathematics and apply mathematics in the contexts of sciences, engineering and other related disciplines. A practising classroom teacher will find this very hard to achieve. On one hand, the mathematics that supports real scientific and engineering applications is often too sophisticated and lies beyond the reach of classroom mathematics – at least as perceived by the teachers. On the other hand, textbook examples that bear some semblance of a real-life application often appear contrived. The teacher’s challenge is to find middle ground that caters for both accessibility and authenticity. This paper situates Gradient Descent, an elementary concept/technique commonly featured in Machine Learning, to create meaningful learning experiences with the aim of connecting topics within mathematics, and with the actual mathematics used to solve real world problems.

We shall explore with technological tools on a problem we posed in [8], which originated from a college practice entrance question (see [3]). In this paper, we are investigating whether a certain closed surface ñ inspired by the aforementioned college exam problem and an a¢ ne transformation ñ is topologically equivalent to a sphere, utilizing the three dimensional visualization capabilities of computer technology to aid our initial analysis. This technological affordance not only makes our investigation accessible to readers with just an undergraduate mathematics background but also allows us to study this surface in detail, finally leading us to a rigorous solution to the problem. Our work highlights the essential role that technology plays in advancing and communicating mathematical research.