– Title: Learning Analytics of student’s learning behaviours in programing education.
– Speaker: TS. Tân Mai Tài, Assistant Professor, School of Computing, Dublin City University, Ireland.
– Time: 10h00, ngày 17 tháng 07 năm 2023 (thứ Hai).
– Location: room 505, building E3, VNU Information Technology Institute, 144 Xuan Thuy street, Hanoi, Vietnam.
In programming education, adequate intervention, such as formative feedback, plays a crucial role in improving students’ academic performance and preparing them for their future roles in the ICT industry. However, providing timely and effective feedback can be challenging for educators, especially when dealing with large quantities of learners’ data collected from various input sources. Several challenges may arise, including data inaccuracies, inconsistencies, noise, and difficulty in integrating data in different structures. Multimodal Learning Analytics (MMLA) is an emerging sub-domain at the intersection of Learning Analytics and Machine Learning that aims to provide a broader understanding of the learning process by leveraging and integrating data from different sources. MMLA focuses on analysing diverse data modalities and signals that may reflect students’ learning behaviour, e.g., audio, video, studying activity data, eye-tracking, user logs and click-stream data etc., to gain a comprehensive view of learning and its various dimensions, which can help educators make informed decisions and hence personalise instruction to suit the individual learner’s needs. This presentation introduces our ongoing research in Educational Data Mining and Learning Analytics, specifically focusing on the collection and analysis of students’ learning
behaviours in programming education. We present key findings, methodologies, and implications for pedagogical practices. Future projects and directions to extend the research to different domains (e.g. medical training) are also discussed.