Enhancing Deep Learning in Geotechnical Engineering through Cognitive Tools and Transmedia Storytelling (Work-in-Progress)

Danielle Gao; Glenn W Ellis; Amy Azogue Irigoyen; Chon-sa Cardona

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: Effective Teaching Potpourri
Tagged Division: Civil Engineering Division (CIVIL)
Page Count: 7
DOI: 10.18260/1-2--56406
Permanent URL: https://peer.asee.org/56406
Download Count: 6

Paper Authors

biography

Danielle Gao Smith College

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Danielle Gao is an undergraduate student majoring in Cognitive Neuroscience and Educational Psychology at Smith College. As a STRIDE scholar, she has worked on this Deep Learning in Geotechnical Engineering project for two years.

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biography

Glenn W Ellis Smith College

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Glenn Ellis is a Professor of Engineering at Smith College who teaches courses in engineering science and methods for teaching science and engineering. He received a B.S. in Civil Engineering from Lehigh University and an M.A. and Ph.D. in Civil Engineeri

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Amy Azogue Irigoyen Smith College

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Chon-sa Cardona Smith College

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Abstract

In recent years, research has shown Imaginative Education (IE) to have significant potential for enhancing student engagement, particularly when integrated with transmedia elements. Findings within K-12 education highlight how these methods foster deeper involvement with course concepts by creating more immersive and interactive learning experiences. Building on this foundation, this study applies the same principles within an undergraduate geotechnical engineering curriculum.

In IE, developmentally appropriate narratives and other cognitive tools (mystery, heroism, extremes of reality, etc.) are used to design learning environments that engage students’ imagination and help structure learning. In a well-designed IE class, instructional strategies such as design thinking, computational thinking, experimentation, discourse, and knowledge-building are all experienced within the context of a story. To fully exploit this pedagogy’s potential, this study uses a transmedia format so that learners will experience different parts of the lesson through multiple media types (text-to-speech, videos, websites, etc.). The ongoing research presented in this paper assesses the potential of IE and transmedia storytelling for increasing student engagement and deep learning as measured by near and far transfer.

In this study, IE and transmedia techniques are incorporated into an introductory geotechnical engineering course. For example, assignments are embedded in an immersive website format in accordance with the storytelling cognitive tool. In a series of videos, a geotechnical engineer walks students through field-work directly on a real job site. These videos combined with soil samples and corresponding data collected from the site are intertwined in a rich narrative; students test soil and report to the engineer their recommendations for the foundation design. Other examples include visual and auditory regeneration of important figures in geotechnical engineering; soil-focused magic tricks that present learners with mysteries to be solved; and a variety of virtual field trips investigating historic sites important to geotechnical engineering.

A data assessment plan employing the research on near and far knowledge transfer has been designed to (1) assess changes in students’ replicative, applicative, and interpretive knowledge before and after taking the course and (2) assess student engagement with the curriculum. The assessment plan includes student surveys, instructor interviews, teacher evaluations, exam performance and open-ended questions measuring if students have the level of interpretive understanding needed for future learning.

Education paradigms must maintain the capacity to meet the current demands of students as a wider range of methods and technologies arise. Although the focus of the study is within the context of geotechnical engineering, increased engagement with any curriculum is associated with deep learning and long-term retention of material essential to all educational fields. Results from this experiment can set a precedent for future application of these techniques in other programs of study.

Citation
Format

Gao, D., & Ellis, G. W., & Azogue Irigoyen, A., & Cardona, C. (2025, June), Enhancing Deep Learning in Geotechnical Engineering through Cognitive Tools and Transmedia Storytelling (Work-in-Progress) Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--56406

TY  - CPAPER
AB  - In recent years, research has shown Imaginative Education (IE) to have significant potential for enhancing student engagement, particularly when integrated with transmedia elements. Findings within K-12 education highlight how these methods foster deeper involvement with course concepts by creating more immersive and interactive learning experiences. Building on this foundation, this study applies the same principles within an undergraduate geotechnical engineering curriculum.

In IE, developmentally appropriate narratives and other cognitive tools (mystery, heroism, extremes of reality, etc.) are used to design learning environments that engage students’ imagination and help structure learning. In a well-designed IE class, instructional strategies such as design thinking, computational thinking, experimentation, discourse, and knowledge-building are all experienced within the context of a story. To fully exploit this pedagogy’s potential, this study uses a transmedia format so that learners will experience different parts of the lesson through multiple media types (text-to-speech, videos, websites, etc.). The ongoing research presented in this paper assesses the potential of IE and transmedia storytelling for increasing student engagement and deep learning as measured by near and far transfer.

In this study, IE and transmedia techniques are incorporated into an introductory geotechnical engineering course. For example, assignments are embedded in an immersive website format in accordance with the storytelling cognitive tool. In a series of videos, a geotechnical engineer walks students through field-work directly on a real job site. These videos combined with soil samples and corresponding data collected from the site are intertwined in a rich narrative; students test soil and report to the engineer their recommendations for the foundation design.
Other examples include visual and auditory regeneration of important figures in geotechnical engineering; soil-focused magic tricks that present learners with mysteries to be solved; and a variety of virtual field trips investigating historic sites important to geotechnical engineering.

A data assessment plan employing the research on near and far knowledge transfer has been designed to (1) assess changes in students’ replicative, applicative, and interpretive knowledge before and after taking the course and (2) assess student engagement with the curriculum. The assessment plan includes student surveys, instructor interviews, teacher evaluations, exam
performance and open-ended questions measuring if students have the level of interpretive understanding needed for future learning.

Education paradigms must maintain the capacity to meet the current demands of students as a wider range of methods and technologies arise. Although the focus of the study is within the context of geotechnical engineering, increased engagement with any curriculum is associated
 
with deep learning and long-term retention of material essential to all educational fields. Results from this experiment can set a precedent for future application of these techniques in other programs of study.

AU  - Danielle Gao
AU  - Glenn W Ellis
AU  - Amy Azogue Irigoyen
AU  - Chon-sa Cardona
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - Enhancing Deep Learning in Geotechnical Engineering through Cognitive Tools and Transmedia Storytelling (Work-in-Progress)

UR  - https://peer.asee.org/56406
DO  - 10.18260/1-2--56406
ER  -