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.
