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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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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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    Summary

    Learning Analytics (LA) can improve veterinary education by identifying at-risk students early. This data-driven approach uses past student performance to predict future success, enabling timely interventions.

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    Area of Science:

    • Veterinary Education
    • Educational Data Mining
    • Learning Analytics

    Background:

    • The education sector increasingly uses big data for decision-making.
    • Data-driven research aids in predicting at-risk students and recommending interventions.
    • Learning Analytics (LA) adoption is limited 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 LA using retrospective data.

    Main Methods:

    • Retrospective data from first-year Doctor of Veterinary Medicine students (Spring 2018) were analyzed.
    • Predictive models were developed for six courses from week 0 to week 14.
    • Weekly prediction results were tracked and compared to actual student performance.

    Main Results:

    • At-risk students were successfully identified early in the semester.
    • Predictive models showed changes in performance predictions throughout the semester.
    • The study validated the utility of LA in identifying students needing additional support.

    Conclusions:

    • Learning Analytics can effectively identify at-risk students in veterinary programs.
    • Early identification allows instructors to provide timely, targeted support.
    • This data-driven approach can enhance student academic success in veterinary medicine.