Hodges receives FRC grant funding for collaborative project with CEIT faculty
College of Education Associate Professor of Instructional Technology Charles Hodges, Ph.D., is participating in a cross-college collaborative research project that recently received nearly $10,000 in internal seed grant funding from Georgia Southern University’s Faculty Research Committee (FRC).
The University’s Faculty Research Committee provides internal funding to promote faculty research and scholarship projects that will lead to future external funding.
College of Engineering and Information Technology faculty Pradipta De, Ph.D., assistant professor of computer sciences (PI) and Aniruddha Mitra, Ph.D., professor of mechanical engineering (co-PI) paired with Hodges (co-PI) to examine the influence of student affect in the learning process.
The project, titled “Modeling Student Affect in the Classroom Teaching Environment,” proposes that learning complex ideas in science, math, engineering and technology requires cognitive skills but is also impacted by the emotional responses of learning. Students who show a positive affect, such as attentiveness or curiosity, have a higher possibility of learning a concept compared to students showing a negative affect, like frustration, boredom or confusion.
The plan is to begin data collection in Mitra’s mechanical engineering courses this fall.
“The computers used during class in the lab will have webcams and open source eye tracking software to track what students are looking at on the computer screens,” said Hodges. “Students will be prompted occasionally to ask if they are engaged or not with the learning materials.”
Hodges explained that the data will then be used with machine learning algorithms to analyze what parts of the course materials are engaging or confusing to students.
“This information can be used to revise and improve the learning materials,” he explained. “It seems reasonable that our results would be useful for other disciplines as well.”
The desired outcome of the study is to build a predictive model, which can help students, as well as the instructor, to be aware of the role of emotion in learning.
“Evaluating instructional materials can be expensive and is always time-consuming,” said Hodges. “This work may provide a more automatic way of capturing data about how students are engaging with course materials, making it easier to target what needs to be improved.”
Data collection is planned to take place for one academic year including the fall 2017 and spring 2018 semesters.
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