Enhancing Deep Learning and Workload Management in Online Education: The Power of Scaffolded Weekly Assessments
Year: 2026
Type: article
Abstract: This study investigates the use of scaffolded weekly assessments with individual feedback in promoting deep learning and managing workload among engineering students in online education. The research focuses on how these assessment strategies shape students’ learning approaches and workload distribution. The study involved the implementation of weekly assessments aligned with intended learning outcomes, complemented by personalized feedback. Data collection comprised student surveys and qualitative feedback to assess the impact on learning approaches and workload management. The qualitative results show that 91.9% of the students adopted deep learning, where only 8.1% engaging in surface learning. Students reported that this approach not only prepared them more effectively for summative evaluations but also contributed to more balanced and efficient workload management. This was particularly noteworthy in the context of online learning, where maintaining student engagement poses additional challenges. The study highlights that structured, feedback-rich assessments can enhance student engagement in online learning environments.
Authors: Maryam Mohammad Zadeh, Rebecca Ferrari
Institution: University of Nottingham
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7124853432