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Empowering Engagement with Learning Analytics-Based Feedback for Programming Education

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationTALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331598419
DOIs
Publication statusPublished - 2025
Event14th International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2025 - Macao, China
Duration: 4 Dec 20257 Dec 2025

Publication series

NameTALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings

Conference

Conference14th International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2025
Country/TerritoryChina
CityMacao
Period4/12/257/12/25

Keywords

  • engagement
  • feedback
  • learning analytics
  • learning strategies
  • selfregulated learning

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