Relationship between deep learning and authentic assessment in undergraduate engineering education

This study aims to explore the relationship between deep learning and authentic assessment in Australian undergraduate engineering education. The hypothesis posits that students will adopt deep learning approaches when engaging with authentic assessment tasks. Drawing from existing literature on student learning approaches, assessment influences, and disciplinary differences, this study aimed to identify the dimensions influencing deep learning, examine student perceptions of authentic assessment, and establish the relationship between assessment types and deep learning factors. After carefully designing the survey questionnaire, pilot testing was conducted, aimed to determine if the questionnaire effectively measures its intended constructs, assess its ease of completion, and identify any areas of confusion. The final online questionnaire was sent to the students of some selected civil and mechanical engineering subjects at QUT. Students received the questionnaire after completing all their assessments of the respective units. Total 72 students responded, among which 37 were found to be complete. The results indicate that authentic assessments, such as group research projects or project-based learning, are considerably more effective in fostering self-directed learning, critical thinking, and knowledge integration compared to traditional assessments like invigilated written exams.