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Identifying Key Learning Factors in Service-Leaning Programs Using Machine Learning

As an impactful experiential learning pedagogy in higher education, service-learning (SL) can enhance students' academic learning and their sense of community and social responsibility by involving them in comprehensive community services. Much extant literature has justified the positive impacts of SL. However, the lack of quantitative analysis on identifying significant learning and course factors that strongly impact students' SL outcomes limits SL's further enhancement and adaptive development. This paper proposes to use machine learning approaches for modeling and identifying key learning factors in SL. We collect and study a large-scale dataset, including students' feedback on learning factors related to the different student experiences, course elements, and self-perceived learning outcomes. Machine learning algorithms are applied to model the various learning factors, contributing to effective classification models that predict students' learning outcomes using their evaluation on the learning factors. The most predictive model is then selected to identify a key set of important variables most indicative to students' SL outcomes. Our experiment results show that learning factors related to study challenges and interactions have significant positive impacts on students' learning gains. We believe that this paper will benefit future studies in this field.