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Robert Plumley

Robert Plumley

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University of North Carolina at Chapel Hill · Health Behavior

Active 2019–2024

h-index4
Citations166
Papers1514 last 5y
Funding
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Research topics

  • Computer Science
  • Psychology
  • Artificial Intelligence
  • Mathematics education
  • Cognitive psychology
  • Applied psychology

Selected publications

  • Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.

    Journal of Educational Psychology · 2023 · 9 citations

    • Psychology
    • Mathematics education
    • Cognitive psychology
  • How do students’ achievement goals relate to learning from well-designed instructional videos and subsequent exam performance?

    Contemporary Educational Psychology · 2023 · 21 citations

    • Computer Science
    • Psychology
    • Artificial Intelligence

    Well-designed instructional videos are powerful tools for helping students learn and prompting students to use generative strategies while learning from videos further bolsters their effectiveness. However, little is known about how individual differences in motivational factors, such as achievement goals, relate to how students learn within multimedia environments that include instructional videos and generative strategies. Therefore, in this study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with instructional videos that required students to answer practice questions between videos, as well as how those activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies.

Frequent coauthors

Education

  • Ph.D. in Education (In Progress), School of Education

    University of North Carolina at Chapel Hill

  • Master of Science in Computer Science, Seidenberg School of Computer Science and Information Systems

    Pace University

    2016
  • B.S. Business Administration, Poole College of Management

    North Carolina State University

    2013
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