2025 ASEE Annual Conference & Exposition

Exploring Collaborative Success of Undergraduate Students within a Cyberlearning Environment Using Gamification

Presented at Computing and Information Technology Division (CIT) Technical Session 6

In higher education, particularly at the undergraduate level, active learning and teamwork have become increasingly important pedagogical approaches. These approaches, including collaborative learning and gamification, aim to enhance student engagement, promote collaboration, and improve learning outcomes. However, the effectiveness of these approaches can vary significantly across different courses and student populations. This variation is due to the manner in which the approaches are implemented, such as the specific class activities, the modality of the class, and the pedagogical tools used to support active learning.

One such tool used in software engineering, software testing, and programming classes at a large urban Hispanic Serving Institution is the CyberLearning Environment (CLE, anonymized for review). CLE provides students and instructors access to vetted learning content through learning objects and tutorials. It also employs three active learning approaches – a lightweight version of collaborative learning (team-based activities), gamification, and social interaction; and can be used in any class independent of the modality. Based on students’ interaction with CLE and the assigned activities, they are awarded virtual points that may be converted to points used towards their course grade.

This paper addresses the challenge of quantifying and analyzing student engagement metrics with advanced clustering techniques. Our research aims to identify distinct student engagement profiles and their correlation with academic performance. We utilize behaviorally based metrics collected through CLE for the past 10 semesters, starting in the spring of 2017, for a software testing class taught by the same instructor. The data collected for each student includes their CLE interactions (the total virtual points awarded, time spent on learning objects, team performance, and number of comments posted) and their course grade data. The insights gained from this study may lead to more personalized learning experiences, enhancing student satisfaction and success rates in team-based learning environments.

Authors

Mr. Gabriel Eduardo Prieto Florida International University [biography]

Gabriel E. Prieto received his BS in Computer Science from Florida International University (FIU) in 2022, and MS degree in Data Science & Artificial Intelligence from FIU in 2024. He is currently the lead iOS developer on an NIH-funded project and anticipates joining a startup company that will leverage the technology developed through this project.
Neila Bennamane Florida International University [biography]

Neila has a Bachelors of Science in Mathematical Sciences and is now pursuing a PhD in Computer Science at Florida International University. Her research ambitions are strongly driven by a desire to bridge the accessibility gap through technological advancements.
Agoritsa Polyzou Florida International University [biography]

Agoritsa Polyzou is an Assistant Professor at the Knight Foundation School of Computing and Information Sciences in Florida International University (FIU), Miami. Agoritsa received the bachelor’s degree in computer engineering and informatics from the University of Patras, Greece, and her Ph.D. degree in computer science and engineering from the University of Minnesota. Next, she was a Postdoctoral Fritz Family Fellow with the Massive Data Institute of McCourt School of Public Policy at Georgetown University, Washington, DC. She is involved in projects in the intersection of education, data mining, machine learning, ethics, and fairness. Her research interests include data mining, recommender systems, predictive models within educational contexts, and the fairness concerns that arise from their use. Her goal is to help students succeed using data and machine learning models.
Dr. Peter J Clarke http://orcid.org/0000-0003-0892-1907 Florida International University [biography]

Peter J. Clarke received his B.Sc. degree in Computer Science and Mathematics from the University of the West Indies (Cave Hill) in 1987, M.S. degree from SUNY Binghamton University in 1996 and Ph.D. in Computer Science from Clemson University in 2003. His research interests are in the areas of software engineering, software testing, model-driven software development, and computer science education. He is currently an associate professor in the Knight Foundation School of Computing and Information Sciences at Florida International University. He is a member of the ACM (SIGSOFT, SIGCSE, and SIGAPP); IEEE Computer Society; and a member of the Association for Software Testing (AST).

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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