Improving Problem-based Learning through Personalized Online Instruction that Promotes Student Engagement, Motivation, and Self-efficacy

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With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by developing instructional support tailored to the needs of individual undergraduate students in introductory engineering courses. In collaboration with Mercy College (a Hispanic Serving Institution), Rowan University will examine the efficacy of combining intelligent tutoring with narrative problem-based simulations to enhance student comprehension of key concepts in electrical and computer engineering. The long-term objective of the project is to develop a digital model of student-teacher classroom interactions that address students' specific difficulties, thereby improving problem-based learning instructional practices. The project aims to personalize online problem-based learning through incorporation of intelligent, online tutoring. In addition, the project plans to develop an artificial intelligence-based facial recognition system to capture fluctuations in learner emotions (e.g., confusion; calm; confidence) during the learning process. The project will conduct research to explore the extent to which personalized, online problem-based learning enhances students' learning and problem-solving ability. Results of this research will provide further insights about the effectiveness of problem-based learning, metacognition, and narrative simulations in undergraduate education. The theoretical framework that guides the work incorporates social cognitive learning theory. A comprehensive and proactive plan for disseminating the results includes seminars, publications, web and social networking presence, online educator support, and K-12 outreach. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the IUSE program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.