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2026 ASEE Annual Conference & Exposition

Writing-to-Learn with Self-Regulation Prompts for Deeper Conceptual Understanding and Higher-Order Skill Development: Implementation, Student Response, and Lessons Learned

Presented at NSF Grantees Poster Session I

Writing-to-Learn (WTL) strategies are widely promoted to support conceptual understanding and self-regulated learning in engineering education, yet classroom-scale evidence remains mixed. This study examines the implementation of a WTL framework with embedded self-regulation prompts in a core undergraduate engineering course across three instructional cycles. The intervention integrated problem rationales, structured learning plans, self-evaluations, and reflective writing within regular coursework, and student responses were evaluated using analytic rubrics aligned with cognitive, metacognitive, and motivational dimensions. Descriptive analyses revealed modest and uneven outcomes. While aggregate performance gains were inconsistent, rubric-level patterns suggested meaningful variation in students’ written reasoning and self-regulatory engagement across cycles. Participation in reflective components declined over time, highlighting sustainability challenges. Rather than advancing strong causal claims, this study contributes implementation-level evidence and practical design insights for integrating WTL with self-regulation prompts in engineering courses.

Authors

Huan Liu Stony Brook University
Prof. Wei Zheng Jackson State University [biography]

Dr. Wei Zheng is a Professor of Civil Engineering at Jackson State University. He received his Ph.D. in Civil Engineering from the University of Wisconsin–Madison in 2001 and has more than ten years of industrial experience before joining academia in 2005. His research mainly focuses on applying probabilistic machine learning to decision-making under uncertainty. His work integrates expertise in civil engineering, AI model development, and educational data analytics to improve engineering decision systems and personalized learning support. His current research in civil engineering focuses on using generative AI technologies to extract knowledge from historical engineering reports and records to support data-driven decision-making. He is also leading two ongoing NSF-funded projects and conducts research in two key areas: (1) Engaging students in open-ended responses and reflective writing to strengthen higher-order thinking and learning skills, and (2) Developing AI-powered automatic assessment tools to evaluate these responses, with the ultimate goal of providing adaptive support with bias mitigation and cultural relevance enhancement through AI-powered education to promote equitable and effective learning.

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