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Adopting Learning Analytics in a First-Year Veterinarian Professional Program: What We Could Know in Advance about

Wenting Weng, Nicola L Ritter, Karen Cornell

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    Learning Analytics in veterinary education can predict at-risk students early. This data-driven approach helps tailor interventions, improving academic success for Doctor of Veterinary Medicine students.

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    Area of Science:

    • Educational Technology
    • Veterinary Education
    • Data Science

    Background:

    • The education sector increasingly uses big data for decision-making.
    • Data-driven research aids in understanding student performance and identifying at-risk students.
    • Learning Analytics applications are scarce in veterinary education.

    Purpose of the Study:

    • To examine the adoption and application of Learning Analytics in a Doctor of Veterinary Medicine program.
    • To demonstrate the predictive capabilities of Learning Analytics for student success.

    Main Methods:

    • Utilized retrospective data from first-year Doctor of Veterinary Medicine students.
    • Developed weekly prediction models for six courses from week 0 to week 14.
    • Analyzed the evolution of prediction accuracy throughout the semester.

    Main Results:

    • Successfully identified at-risk students at an early stage using weekly prediction models.
    • Demonstrated the correlation between predicted performance and actual student outcomes.
    • Showcased the practical application of Learning Analytics in a veterinary curriculum.

    Conclusions:

    • Learning Analytics can be effectively adopted in veterinary education.
    • Early identification of at-risk students enables timely interventions.
    • This data-driven approach supports enhanced teaching and learning for improved academic success.