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FEAL: Fine-Grained Evaluation of Active Learning in Collaborative Learning Spaces

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Instrument Development

Tagged Division

Educational Research and Methods

Tagged Topic

Diversity

Page Count

16

DOI

10.18260/1-2--28353

Permanent URL

https://peer.asee.org/28353

Download Count

243

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Paper Authors

biography

Sixing Lu University of Arizona

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Sixing Lu is a PhD candidate of Electrical and Computer Engineering department of University of Arizona.

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Loukas Lazos University of Arizona

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Loukas Lazos is a faculty member of the Electrical and Computer Engineering Department at the University of Arizona. Before joining the University of Arizona, he was a co-director of the Network Security Lab at the University of Washington. He received my PhD. and M.S. degrees in Electrical Engineering at the University of Washington. he obtained his undergraduate diploma in Electrical and Computer Engineering at the National Technical University of Athens, Greece. His current research interests are in the areas of security and privacy, networking, and wireless communications, focusing on identification, modeling and mitigation of network security vulnerabilities, visualization of network threats, and analysis of network performance.

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Roman Lysecky University of Arizona

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Roman Lysecky is an Associate Professor of Electrical and Computer Engineering at the University of Arizona. He received his Ph.D. in Computer Science from the University of California, Riverside in 2005. His research interests include embedded systems, runtime optimization, non-intrusive system observation methods, data-adaptable systems, and embedded system security. He has recently coauthored multiple textbooks, published by zyBooks, that utilize a web-native, interactive, and animated approach, which has shown notable increases in student learning and course grades.

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Abstract

Paper type: Evidence-Based Practice

Numerous studies have shown the effectiveness of collaborative active-learning pedagogies compared to traditional lectures across STEM fields. However, incorporating active learning in large classes presents unique challenges in stimulating student engagement by developing quality activities. A growing trend at universities is to create collaborative learning spaces (CLSs) that are purposefully designed and equipped to facilitate active learning. While some research has identified learning activities that are effective in CLS environments based on learning psychology, the success of individual activities is neither defined nor measured. This gap in knowledge is often met with a trial and error approach over the course of numerous semesters and adjustments are made based on instructors' partial perceptions. But activity effectiveness is not evaluated and correlated to student performance.

We present a novel measurement instrument called Fine-grained Evaluation of Active Learning (FEAL) for large CLS-based classes. FEAL can be quickly administered by preceptors to record key measures of activity success such as student engagement, activity difficulty, associated lecture time, and activity time. Other relevant information such as the concepts covered by the activity and the activity type are also recorded to be later cross-analyzed. FEAL can be applied to code exam questions and to assess student performance for the same concepts. We applied FEAL to a large freshman-level computer programming course with a student enrollment of 200 over the course of one semester. We present an overview of FEAL, its administration process within the CLS, and a detailed account of our evaluation methodology. We also highlight key lessons learned and conclusions drawn on the success or failure of individual activities, and outline planned improvements to in-class activities based on the obtained results.

Lu, S., & Lazos, L., & Lysecky, R. (2017, June), FEAL: Fine-Grained Evaluation of Active Learning in Collaborative Learning Spaces Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28353

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