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Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome

Author

Listed:
  • John C. Yi

    (Department of Decision and System Sciences, Haub School of Business, Saint Joseph’s University, Philadelphia, PA 19131, USA)

  • Christina D. Kang-Yi

    (Center for Mental Health Policy and Services Research, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19131, USA)

  • Flavia Burton

    (Department of Decision and System Sciences, Haub School of Business, Saint Joseph’s University, Philadelphia, PA 19131, USA)

  • H. David Chen

    (Department of Decision and System Sciences, Haub School of Business, Saint Joseph’s University, Philadelphia, PA 19131, USA)

Abstract

The application of predictive analytics in higher education has increasingly gained acceptance and interest over the years. In this study, a predictive model is developed to map students’ non-cognitive skills against their class performance. Our predictive analytics model identified the non-cognitive skills that predicted new students’ class performance based on the dataset collected early in the semester. Based on the predictive analytics results, tailored teaching to improve students’ non-cognitive skills was offered in a required class designed for undergraduate business students. The improvement in the average final semester grade for students in the tailored-taught classes based on our predicted analytics approach was 9%, which was higher than that of the class grade taught without the approach. The study finding also demonstrates a long-term, sustainable positive effect to the students with the predictive analytics approach.

Suggested Citation

  • John C. Yi & Christina D. Kang-Yi & Flavia Burton & H. David Chen, 2018. "Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4012-:d:180056
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    References listed on IDEAS

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    1. Hurd, Michael D, 1999. "Anchoring and Acquiescence Bias in Measuring Assets in Household Surveys," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 111-136, December.
    2. Kaisu Sammalisto & Agneta Sundström & Robin Von Haartman & Tove Holm & Zhilei Yao, 2016. "Learning about Sustainability—What Influences Students’ Self-Perceived Sustainability Actions after Undergraduate Education?," Sustainability, MDPI, vol. 8(6), pages 1-16, May.
    3. Hurd, Michael D, 1999. "Anchoring and Acquiescence Bias in Measuring Assets in Household Surveys," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 111-136, December.
    4. John C. Yi & Sungho Kim, 2016. "Early Adoption of Innovative Analytical Approach and Its Impact on Organizational Analytics Maturity and Sustainability: A Longitudinal Study from a U.S. Pharmaceutical Company," Sustainability, MDPI, vol. 8(8), pages 1-19, August.
    5. Kahneman, Daniel, 1992. "Reference points, anchors, norms, and mixed feelings," Organizational Behavior and Human Decision Processes, Elsevier, vol. 51(2), pages 296-312, March.
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    Cited by:

    1. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2019. "Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment," Sustainability, MDPI, vol. 11(24), pages 1-12, December.
    2. Bo Jiang & Yanbai He & Rui Chen & Chuanyan Hao & Sijiang Liu & Gangyao Zhang, 2020. "Progressive Teaching Improvement For Small Scale Learning: A Case Study in China," Future Internet, MDPI, vol. 12(8), pages 1-15, August.
    3. Monica Mihaela Maer-Matei & Cristina Mocanu & Ana-Maria Zamfir & Tiberiu Marian Georgescu, 2019. "Skill Needs for Early Career Researchers—A Text Mining Approach," Sustainability, MDPI, vol. 11(10), pages 1-17, May.

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