Supporting Large-Scale Engineering Education: The Active Learning Personal Advisor via Course Automatio

Public education is on the brink of a potential crisis attempting to significantly increase student enrollment while maintaining quality of education. Online courses have been proposed and debated among members of the UC regents, numerous college administrators, faculty, and students. On one hand, online education can reduce overhead while enrolling more students. Directly translating the classroom lectures and materials to an online environment does not necessarily produce equivalent student performance and satisfaction from the course compared to an in-class environment. Since there is no universal standard for online education, erratic and inconsistent results have been achieved in terms of student performance and costs to students as well as administration. A hybrid scalable teaching and learning methodology is required by both educators and students to achieve the greatest advantages of using today's technology and to apply it toward improving student performance and participation.;This dissertation presents a methodology and system to provide a more individualized and responsive learning environment for students in large hybrid and online university courses while keeping overall costs and time commitment down as well as improve overall student performance. The Active Learning Personal Advisor, the implemented learning design tool of this research, is developed based on multi-disciplinary metrics and studies from the fields of Psychology, Education, and Engineering. A primary limiting resource for both students and instructors is time. By automating some basic key interactions that may occur between students and instructors, hours of each individual's time can be saved, maximizing the quality of the available in-person interactions to occur during a course while allowing for a more scalable sized classroom environment.