Bridging the gap between evidence-informed and actual teaching practices of engineering educators: an AI-enhanced professional learning system

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Imagine a classroom where engineering students are challenged to apply what they’re learning, where they interactively explore the complexities of authentic, level-appropriate engineering problems, supported by professors who are aware of and apply evidence-informed teaching practices. Expectations align with the engineering workplace. Learners improve their acquired knowledge and skills through experimentation and deliberate practice. They harness systems thinking as they make connections and see patterns. They are challenged to adapt to whatever scenario they face, to identify problems, think critically, generate and model effective solutions, and to make justifiable decisions. Learners experience the tension between knowing and doing engineering things. They learn firsthand, and in context, what it means to be a practicing engineer. This aspiring approach is very different from the didactic practices reported in most Canadian undergraduate engineering classrooms. The challenge, and the focus of this research, is to encourage and assist engineering educators to stretch their current teaching practices beyond what’s comfortable and customary, to those that are both evidence-informed and truly representative of engineering. This research is a blend of interdisciplinary mixed-methods and design-based research. The interdisciplinary mixed-method research integrates the findings of educational research, learning sciences, professional learning, and systems thinking. Sixteen research studies explore the experiences and practices of educators and students in the Canadian undergraduate engineering system. These findings confirm that a gulf exists between evidence-informed teaching practices and what happens in the typical undergraduate engineering classroom. They clearly establish the need for an educational development model that translates existing educational research into tangible, level-appropriate teaching practices for engineering educators at all levels of experience and skill. This foundational research leads to the design and development of this thesis' first of three contributions, the LENS (Learning Environments Nurture Success) model of engineering faculty development. This model, which is comprised of six lenses that align with an effective learning environment, offers a practical framework to support educational development and planning for all forms of delivery (face-to-face, remote, blended, or hybrid). It can be used independently, in consultation with an educational developer, or in collaboration with colleagues. It threads educator-related threshold concepts associated with learning, pedagogy, assessment, and teaching with technology through each of six lenses, and links myriad interdisciplinary research findings to facilitate the successful education of undergraduate engineering students. The second contribution of this research is a proof-of-concept intelligent Professional Learning System (iPLS). This AI-enhanced learning platform individualizes and guides the development of professional knowledge and skills. The look, feel, and functionality of this proof-of-concept iPLS is shaped by an integration of research findings in professional learning, training and development, technology-based learning, and AI in education. The final contribution of this work is an iPLS application designed to help engineering educators develop their teaching practices. It provides needs-specific recommendations based on an individual's ranking on a novice to expert continuum and achieved teaching-related thresholds. Quantitative and qualitative field test results show the combined LENS, iPLS, and engineering education application (EEA) to be a viable method by which engineering educators can stretch their teaching to include more evidence-informed teaching practices. Using the elements of an elegant design as its measure, the system is determined to be effective and robust with a minimal number of unexpected consequences.
engineering education, educational development, AI-enhanced learning system, STEM, faculty development, Artificial Intelligence, recommendation system, iPLS, LENS model, evidence-informed teaching, teaching practices, threshold concepts, 3M^2 framework, DUAL framework, professional learning, teaching expertise, teaching-related threshold concepts, design-related threshold concepts
Nelson, N. L. (2023). Bridging the gap between evidence-informed and actual teaching practices of engineering educators: an AI-enhanced professional learning system (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from