Document Type

Dissertation

Date of Award

8-31-2021

Degree Name

Doctor of Philosophy in Information Systems - (Ph.D.)

Department

Informatics

First Advisor

Michael Bieber

Second Advisor

Starr Roxanne Hiltz

Third Advisor

Jeffrey Hsu

Fourth Advisor

Michael J. Lee

Fifth Advisor

Roberta Schorr

Sixth Advisor

Margarita Vinnikov

Seventh Advisor

Donghee Yvette Wohn

Abstract

Participatory Learning (PL) integrates several learning approaches, engaging students throughout the entire assignment process for both online and face-to-face courses. Beyond simply providing a solution, students also craft a problem (problem-based learning), grade each other (peer assessment and feedback), evaluate themselves (self-assessment), and can view others’ work (learning by example). This dissertation research explores the resulting learning effects. Contributions to both educational and Information Systems research include extending an early PL model and experiments that applied the PL approach to examinations, by validating and testing new constructs based on user activity and critical thinking. In addition, the study explores a microlearning condition. The study found that the majority of the students enjoyed being part of the PL approach for assignments while also perceiving learning benefits. Students reported learning from crafting problems, solving problems, grading and reading others’ work. The extended PL model was tested and partially validated using Partial Least Squares path modeling and analysis. Recommendations for future work include improving the PL support website and the study protocol. PL has the potential to change the way students engage with their peers and assignments, thereby improving their critical thinking across many disciplines at the university level.

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