Transforming Pedagogical Assessment: AI and Computer Vision-Enhanced Classroom Observations for Experiment-Centric Learning Environments

Blessing Isoyiza Adeika; Pelumi Olaitan Abiodun; Oludare Adegbola Owolabi

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: ELOS Technical Session 2 - Beliefs, Motivation, and Pedagogy
Tagged Division: Experimentation and Laboratory-Oriented Studies Division (DELOS)
Page Count: 17
DOI: 10.18260/1-2--48175
Permanent URL: https://peer.asee.org/48175
Download Count: 99

Paper Authors

biography

Blessing Isoyiza Adeika Morgan State University orcid.org/0009-0007-5600-8051

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Blessing ADEIKA is a Doctoral student at Morgan State University currently in the Doctor of Engineering Program. She has an interest in teaching student basic concepts by adopting an Experiment-centric approach to it. She also is currently working towards being a Data Scientist - AI/ML Expert and hopes to use her skills to proffer solutions in the Medical, Financial, Technology and any other Sector she sees a need to be filled/catered for.

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Pelumi Olaitan Abiodun Morgan State University orcid.org/0000-0002-4969-9591

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Pelumi Abiodun is a current doctoral student and research assistant at the department of Civil Engineering, Morgan State University, Baltimore, Maryland. Pelumi got his BSc and MSc degree in Physics from Obafemi Awolowo University, where he also served as a research assistant at the Environmental Pollution Research unit, in Ile-Ife, Nigeria. As part of his contribution to science and engineering, Pelumi has taught as a teaching assistant both at Morgan State University and Obafemi Awolowo University. With passion to communicate research findings and gleaned from experts in the field as he advances his career, Olaitan has attended several in-persons and virtual conferences and workshop, and at some of them, made presentation on findings on air pollution, waste water reuse, and heavy metal contamination.

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Oludare Adegbola Owolabi P.E. Morgan State University

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Dr. Oludare Owolabi, a professional engineer in Maryland, joined the Morgan State University faculty in 2010. He is the assistant director of the Center for Advanced Transportation and Infrastructure Engineering Research (CATIER) at Morgan State Universit

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Abstract

This paper presents an innovative approach to revolutionizing STEM education by seamlessly integrating artificial intelligence (AI) into the assessment of experiment-centric pedagogy. Our research spans diverse disciplines, including biology, chemistry, physics, civil engineering, transportation engineering, mathematics, and computer science. We've transitioned from traditional teaching methods to an immersive approach, embedding experiments into core curriculum modules to convey essential concepts effectively. Initially, this study employed the Laboratory Observation Protocol for Undergraduate STEM (LOPUS) and later transitioned to the Classroom Observation Protocol for Undergraduate STEM (COPUS), relying on manual observations. Dedicated spaces on sheets were marked at two-minute intervals to record student and instructor activities. This study proposes a transformative leap forward, introducing an AI-based model to automate the observation process. Our primary goal is to develop a sophisticated deep learning model capable of autonomously tracking and documenting a wide range of activities performed by students and instructors in the classroom. This model will recognize, and document 26 distinct activity constructs evenly distributed between students and instructors, encompassing student questioning frequency, instructor lecturing intervals, and student-led discussions. Leveraging state-of-the-art AI technologies, we aim to enhance the efficiency, precision, and scalability of pedagogical assessment, providing educators with invaluable insights into the dynamics of the learning environment. Our research extends beyond assessment to measure student engagement within experiment-centric classes, including the frequency of student questions, their predictive abilities concerning experimental outcomes, and participation in discussions. In conclusion, our research drives a transformative shift in STEM education, offering a novel framework for precise assessment, personalization, and instructional enhancement. This advancement empowers educators to refine teaching strategies, enhancing student engagement, and creating a dynamic and immersive learning environment. Furthermore, the AI-based model complements existing observation protocols, like COPUS, potentially serving as a valuable control measure for assessing classroom activities.

Citation
Format

Adeika, B. I., & Abiodun, P. O., & Owolabi, O. A. (2024, June), Transforming Pedagogical Assessment: AI and Computer Vision-Enhanced Classroom Observations for Experiment-Centric Learning Environments Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--48175

TY - CPAPER
AB - This paper presents an innovative approach to revolutionizing STEM education by seamlessly integrating artificial intelligence (AI) into the assessment of experiment-centric pedagogy. Our research spans diverse disciplines, including biology, chemistry, physics, civil engineering, transportation engineering, mathematics, and computer science. We&#39;ve transitioned from traditional teaching methods to an immersive approach, embedding experiments into core curriculum modules to convey essential concepts effectively.
Initially, this study employed the Laboratory Observation Protocol for Undergraduate STEM (LOPUS) and later transitioned to the Classroom Observation Protocol for Undergraduate STEM (COPUS), relying on manual observations. Dedicated spaces on sheets were marked at two-minute intervals to record student and instructor activities.
This study proposes a transformative leap forward, introducing an AI-based model to automate the observation process. Our primary goal is to develop a sophisticated deep learning model capable of autonomously tracking and documenting a wide range of activities performed by students and instructors in the classroom. This model will recognize, and document 26 distinct activity constructs evenly distributed between students and instructors, encompassing student questioning frequency, instructor lecturing intervals, and student-led discussions.
Leveraging state-of-the-art AI technologies, we aim to enhance the efficiency, precision, and scalability of pedagogical assessment, providing educators with invaluable insights into the dynamics of the learning environment. Our research extends beyond assessment to measure student engagement within experiment-centric classes, including the frequency of student questions, their predictive abilities concerning experimental outcomes, and participation in discussions.
In conclusion, our research drives a transformative shift in STEM education, offering a novel framework for precise assessment, personalization, and instructional enhancement. This advancement empowers educators to refine teaching strategies, enhancing student engagement, and creating a dynamic and immersive learning environment. Furthermore, the AI-based model complements existing observation protocols, like COPUS, potentially serving as a valuable control measure for assessing classroom activities.
AU - Blessing Isoyiza Adeika
AU - Pelumi Olaitan Abiodun
AU - Oludare Adegbola Owolabi P.E.
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Transforming Pedagogical Assessment: AI and Computer Vision-Enhanced Classroom Observations for Experiment-Centric Learning Environments
UR - https://peer.asee.org/48175
DO - 10.18260/1-2--48175
ER -