Study Approaches and Academic Performance: an Empirical Study Among Chinese Engineering Undergraduates

• In Event: Improvement of Education and Instruction in Universities in China

Proposal

Background: The learning approaches of university students significantly impact their overall learning achievements. Although existing research has predominantly focused on the effects of deep and surface learning approaches on academic performance, a prevailing performance-oriented mindset in students leads them to pursue high GPA and adopt learning strategies that are utilitarian and strategic. Unfortunately, the impact of learning approaches on research output and the role of strategic learning approaches in this context have received limited attention. This study aims to investigate the effects and underlying mechanisms of deep, strategic, and surface learning approaches on learning outcomes, as well as the factors contributing to the formation of different learning approaches.

Methods: The study first collected cross-sectional data from 692 engineering students in a prestigious "Double First-Class" university in China. The data included socio-demographic characteristics, learning outcomes, and scores from a simplified learning approach scale. Using multiple linear regression, poisson regression, and logit regression, we analyzed the impact of different learning approaches on learning outcomes. Additionally, to gain deeper insights into the formation of learning approaches, we conducted interviews with 15 purposively selected participants.

Results: The findings revealed that deep learning approaches were predominant among engineering students, and learning approaches had multidimensional effects on their learning outcomes. Deep learning significantly improved research output and also showed a positive effect on academic performance, although the latter was not statistically significant. Meanwhile, strategic learning approaches positively affected academic performance but were found to suppress improvements in research output. Surface learning approaches were found to hinder academic performance improvements. The positive impact of deep learning on learning outcomes was attributed to factors such as sustained interest, immersive engagement, autonomous planning, critical scrutiny, and divergent thinking. Strategic learning, while improving academic performance, carried the risk of leading students towards behaviors that hindered their research output improvements. Surface learning, on the other hand, interfered with learning outcomes by causing disinterest, detachment, and mechanical memorization. The formation and reinforcement of learning approaches were closely tied to societal role expectations, evaluation standards, learning habits from early education stages, university evaluation systems, and teaching environments.

Conclusion: The study highlights that deep learning improves research output as well as academic performance for engineering students. Strategic learning approaches positively influences academic performance but may inadvertently hinder research output. The strategies employed by students to improve academic performance may lead to behaviors that detract from their overall learning experience, impeding research output improvements. Surface learning, by hindering academic performance, calls for attention to foster a deeper understanding and engagement in learning. The formation and reinforcement of learning approaches are intricately connected to students' individual characteristics, prior learning experiences, and the overall educational environment provided by the university. In particular, high-quality teaching that stimulates students' interest and encourages tolerance for knowledge uncertainty is identified as a key factor in promoting deep learning and enhancing learning outcomes.

Authors

• Wenjuan Gao, Beihang University

• Junlin Ji, Beihang University

• Yue Qu, Beihang University