Exploring the connection between deep learning and learning assessments: a cross-disciplinary engineering education perspective

Abstract

It is widely accepted that student learning is significantly affected by assessment methods, but a concrete relationship has not been established in the context of multidisciplinary engineering education. Students make a physiological investment and internalize learning (deep learning) if they see high value in their learning. They persist despite challenges and take delight in accomplishing their work. As student deep learning is affected by the assessment system, it is important to explore the relationship between assessment systems and factors affecting deep learning. This study identifies the factors associated with deep learning and examines the relationships between different assessment systems those factors. A conceptual model is proposed, and a structured questionnaire was designed and directed to 600 Queensland University of Technology (QUT) multidisciplinary engineering students, with 243 responses received. The gathered data were analyzed using both SPSS and SEM. Exploratory factor analysis revealed that deep learning is strongly associated with learning environment and course design and content. Strong influence of both summative and formative assessment on learning was established in this study. Engineering educators can facilitate deep learning by adopting both assessment types simultaneously to make the learning process more effective. The proposed theoretical model related to the deep learning concept can support the key practices and modern learning methodologies currently adopted to enhance the learning and teaching process.