Discovery Learning: Development of a Unique Active Learning Environment for Introductory Chemistry
MetadataShow full item record
Type of Work20 pages
Citation of Original PublicationOtt, L., Carpenter, T., Hamilton, D., & LaCourse, W. (2018). Discovery Learning: Development of a Unique Active Learning Environment for Introductory Chemistry. Journal of the Scholarship of Teaching and Learning, 18(4). https://doi.org/10.14434/josotl.v18i4.23112
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
process-oriented guided inquiry learning (POGIL)
student-centered active learning environment with upside-down pedagogies (SCALE-UP)
It is well established that active learning results in greater gains in student conceptual knowledge and retention compared to traditional modes of learning. However, active learning can be very difficult to implement in a large-enrollment course due to various course and institutional barriers. Herein, we describe the development and implementation of Discovery Learning, a novel active learning discussion/recitation for a large enrollment general chemistry course. Drawing on the very successful cooperative learning pedagogies Process-Oriented Guided Inquiry Learning (POGIL) and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE_UP), Discovery Learning involves students working in self-managed teams on inquiry problems in a unique learning environment, the Chemistry Discovery Center. In this case study, we will describe the design and implementation of Discovery Learning and report data on its successes, which include increased student performance and retention.
Showing items related by title, author, creator and subject.
Ramamurthy, Sreenivasan Ramasamy; Roy, Nirmalya (Wiley, 2018)There has been an upsurge recently in investigating machine learning techniques for Activity Recognition (AR) problems as that have been very effective in extracting and learn-ing knowledge from the activity datasets. The ...
Hossain, H. M. Sajjad; Khan, Abudullah al Hafiz; Roy, Nirmalya; Pan, Shimei (ACM, 2018)Deep learning architectures have been applied increasingly in multi-modal problems which has empowered a large number of application domains needing much less human supervision in the process. As unlabeled data are abundant ...