Using attention-based neural networks for predicting student learning outcomes in service-learning

Yujun Fu, Grace Ngai (Corresponding Author), Hong Va Leong, Chi Fai Stephen CHAN, Daniel T.L. Shek

Research output: Journal article publicationJournal articleAcademic researchpeer-review


As a high-impact educational practice, service-learning has demonstrated success in positively influencing students’ overall development, and much work has been done on investigating student learning outcomes from service-learning. A particular direction is to model students’ learning outcomes in the context of their learning experience, i.e., the various student, course, and pedagogical elements. It contributes to a better understanding of the learning process, a more accurate prediction of students’ attainments on the learning outcomes, and improvements in the design of learning activities to maximize student learning. However, most of the existing work in this area relies on statistical analysis that makes assumptions about attribute independence or simple linear dependence, which may not accurately reflect real-life
scenarios. In contrast, the study described in this paper adopted a neural network based approach to investigate the impact of students’ learning experience on different service-learning outcomes. A neural network with attention mechanisms was constructed to predict students’ service-learning outcomes by modeling the contextual information from their various learning experiences. In-depth evaluation experiments on a large-scale dataset collected from more than 10,000 students showed that this proposed model achieved better accuracy on predicting service-learning outcomes. More importantly, it could capture the interdependence between different aspects of student learning experience and the learning outcomes. We believe that this framework can be extended to student modeling for other types of learning activities
Original languageEnglish
JournalEducation and Information Technologies
Publication statusPublished - 1 Apr 2023


  • Computational modeling
  • Learning experience
  • Learning outcomes
  • Service-learning
  • Neural networks

ASJC Scopus subject areas

  • Education


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