Enhancing Student Feedback Using Predictive Models In Visual Literacy Courses

In the evolving landscape of educational technology, data visualization plays a pivotal role in higher education. While peer review is an established pedagogical tool that actively engages students, its long-term effectiveness, particularly when integrated with data-driven predictive modeling for analyzing student comments, has not been empirically validated, especially in the context of data visualization courses. This study aims to fill this gap by employing Naive Bayes modeling to analyze peer review data from an undergraduate visual literacy course over a five-year period (2017-2022). Building on the research of Friedman and Rosen, as well as Beasley et al., our study not only reaffirms the utility of Naive Bayes modeling in analyzing student comments, particularly focusing on parts of speech with nouns as the prominent category but also explores its application in enhancing the peer review process. A key finding is the emphasis on the ‘lie factor’ in students’ comments when using the visual peer review rubric, highlighting areas for potential course content adaptation and instructional refinement. Comparing the Naive Bayes model with Beasley’s approach, we find that while both methodologies aid instructors in mapping classroom dynamics, the Naive Bayes model offers a more detailed framework for predictive analysis. Our findings suggest that predictive modeling, as a tool to assess student comments, can provide novel insights into visual peer review. This could lead to impactful changes in course content, project modifications, and rubric enhancements, ultimately benefiting student learning and engagement in visual literacy courses.

Alon Friedman, Kevin Hawley, Paul Rosen, and Md Dilshadur Rahman. Enhancing Student Feedback Using Predictive Models In Visual Literacy Courses. IEEE Global Engineering Education Conference, 2024.

@inproceedings{friedman2024enhancing,
  title = {Enhancing Student Feedback Using Predictive Models in Visual Literacy Courses},
  author = {Friedman, Alon and Hawley, Kevin and Rosen, Paul and Rahman, Md Dilshadur},
  booktitle = {IEEE Global Engineering Education Conference},
  series = {EDUCON},
  year = {2024},
  abstract = {In the evolving landscape of educational technology, data visualization
    plays a pivotal role in higher education. While peer review is an established
    pedagogical tool that actively engages students, its long-term effectiveness,
    particularly when integrated with data-driven predictive modeling for analyzing student
    comments, has not been empirically validated, especially in the context of data
    visualization courses. This study aims to fill this gap by employing Naive Bayes
    modeling to analyze peer review data from an undergraduate visual literacy course over a
    five-year period (2017-2022). Building on the research of Friedman and Rosen, as well as
    Beasley et al., our study not only reaffirms the utility of Naive Bayes modeling in
    analyzing student comments, particularly focusing on parts of speech with nouns as the
    prominent category but also explores its application in enhancing the peer review
    process. A key finding is the emphasis on the 'lie factor' in students' comments when
    using the visual peer review rubric, highlighting areas for potential course content
    adaptation and instructional refinement. Comparing the Naive Bayes model with Beasley’s
    approach, we find that while both methodologies aid instructors in mapping classroom
    dynamics, the Naive Bayes model offers a more detailed framework for predictive
    analysis. Our findings suggest that predictive modeling, as a tool to assess student
    comments, can provide novel insights into visual peer review. This could lead to
    impactful changes in course content, project modifications, and rubric enhancements,
    ultimately benefiting student learning and engagement in visual literacy courses.}
}