TY - GEN
T1 - Empowering Engagement with Learning Analytics-Based Feedback for Programming Education
AU - Chan, Calana Mei Pou
AU - Wong, Un Hong
AU - Lam, Chi Kin
AU - Pang, Patrick Cheong Iao
AU - Mendes, António José
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This Research Full Paper investigates the effect of learning analytics-based feedback on enhancing engagement and guiding the adaptation of learning strategies of programming students. Feedback as a driving force to foster self-regulation has been widely studied. When designed and implemented strategically, feedback can strengthen motivation and enhance engagement, empowering students to take an active role in their learning through effective cognitive and metacognitive processes. By supporting students to monitor and regulate their learning through planning and goal-setting, and the adaptation of appropriate learning strategies, feedback can have a tremendous benefit on students' learning and skill development. This study utilises learning analytics from a learning management system to derive feedback reporting on students' engagement level with the learning materials. It evaluates the impact of this feedback intervention on enhancing student engagement and academic achievement in programming education. Students learning to program often face numerous challenges in gaining proficiency, and high failure rates are typical in introductory programming courses. This study explores the relationship between engagement feedback intervention and the application of learning strategy through the lens of self-determination theory. The proposed feedback intervention aims to guide novice programming students in adapting the metacognitive strategy of planning and goal-setting, which can ultimately enhance their engagement and learning performance. The results of this study provide empirical evidence from learning analytics to highlight the power of engagement feedback in guiding the adaptation of a learning strategy. This provides educators with insights into designing a feedback strategy as an instrumental tool to help students internalise the use of appropriate learning strategies, particularly for students learning programming, which can, in turn, lead to better learning outcomes.
AB - This Research Full Paper investigates the effect of learning analytics-based feedback on enhancing engagement and guiding the adaptation of learning strategies of programming students. Feedback as a driving force to foster self-regulation has been widely studied. When designed and implemented strategically, feedback can strengthen motivation and enhance engagement, empowering students to take an active role in their learning through effective cognitive and metacognitive processes. By supporting students to monitor and regulate their learning through planning and goal-setting, and the adaptation of appropriate learning strategies, feedback can have a tremendous benefit on students' learning and skill development. This study utilises learning analytics from a learning management system to derive feedback reporting on students' engagement level with the learning materials. It evaluates the impact of this feedback intervention on enhancing student engagement and academic achievement in programming education. Students learning to program often face numerous challenges in gaining proficiency, and high failure rates are typical in introductory programming courses. This study explores the relationship between engagement feedback intervention and the application of learning strategy through the lens of self-determination theory. The proposed feedback intervention aims to guide novice programming students in adapting the metacognitive strategy of planning and goal-setting, which can ultimately enhance their engagement and learning performance. The results of this study provide empirical evidence from learning analytics to highlight the power of engagement feedback in guiding the adaptation of a learning strategy. This provides educators with insights into designing a feedback strategy as an instrumental tool to help students internalise the use of appropriate learning strategies, particularly for students learning programming, which can, in turn, lead to better learning outcomes.
KW - engagement
KW - feedback
KW - learning analytics
KW - learning strategies
KW - selfregulated learning
UR - https://www.scopus.com/pages/publications/105033231812
U2 - 10.1109/TALE66047.2025.11346593
DO - 10.1109/TALE66047.2025.11346593
M3 - Conference contribution
AN - SCOPUS:105033231812
T3 - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
BT - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2025
Y2 - 4 December 2025 through 7 December 2025
ER -