The Role of Conation in Student Performance and Perceptions in Self-Paced Courses
Nationwide, colleges and universities are increasingly adopting intelligent learning software to incorporate self-paced components into introductory STEM courses. Student success in these courses depends not only on the previous knowledge base of the student, but also on that student's work patterns.
Our pilot studies in precalculus indicate that a student's Kolbe A™ Index may serve as a predictor for work patterns in such courses. If larger-scale studies support our initial observations, this will serve as a means of developing interventions for students who have adequate incoming knowledge, but whose natural work patterns put them at risk of performing poorly in self-paced courses.
Initial results from the pilot studies can be found in:
Khushikumari Patel, Claire Dancz, Eliza Gallagher, and Charity Watson. Work in Progress: Understanding how Action Modes® can help or hinder students in self-paced courses. Proceedings of the 2017 American Society of Engineering Education Annual Conference and Exposition, Columbus, OH, 2017.
We are actively seeking collaborators for a larger scale study. If you are interested in collaborating, please complete this survey:
Project team members: Eliza Gallagher, Claire Dancz, Khushi Patel,
Charity Watson
The views expressed on this page are those of the author, and do not necessarily reflect the views of Clemson University or any of the Departments with which the author is affiliated.