IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p4012-d180056.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/4012/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/11/4012/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    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. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Butler, Monika & Teppa, Federica, 2005. "Should You Take a Lump-Sum or Annuitize? Results from Swiss Pension Funds," CEPR Discussion Papers 5316, C.E.P.R. Discussion Papers.
    2. van Soest, Arthur & Hurd, Michael, 2008. "A Test for Anchoring and Yea-Saying in Experimental Consumption Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 126-136, March.
    3. Winter, Joachim, 0000. "Bracketing effects in categorized survey questions and the measurement of economic quantities," Sonderforschungsbereich 504 Publications 02-35, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    4. R Alessie & A Kapteyn, 2001. "New data for understanding saving," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 17(1), pages 55-69, Spring.
    5. Fabian Gouret & Guillaume Hollard, 2011. "When Kahneman meets Manski: Using dual systems of reasoning to interpret subjective expectations of equity returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(3), pages 371-392, April.
    6. Erin Ruel & Robert Hauser, 2013. "Explaining the Gender Wealth Gap," Demography, Springer;Population Association of America (PAA), vol. 50(4), pages 1155-1176, August.
    7. David Comerford & Liam Delaney & Colm Harmon, 2009. "Experimental Tests of Survey Responses to Expenditure Questions," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 419-433, December.
    8. van Soest, A.H.O. & Hurd, M., 2004. "Models for Anchoring and Acquiescence Bias in Consumption Data," Other publications TiSEM 45bba4af-d462-4b9f-a064-b, Tilburg University, School of Economics and Management.
    9. Dolan, Paul & Metcalfe, Robert, 2012. "The relationship between innovation and subjective wellbeing," Research Policy, Elsevier, vol. 41(8), pages 1489-1498.
    10. Alison J. Wellington & Justin B. Whitmire, 2007. "Kidney Transplants And The Shortage Of Donors: Is A Market The Answer?," Contemporary Economic Policy, Western Economic Association International, vol. 25(2), pages 131-145, April.
    11. John L. Czajka & Gabrielle Denmead, "undated". "Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys," Mathematica Policy Research Reports 19724257b78544bdbd55f15be, Mathematica Policy Research.
    12. Kremslehner, Daniela & Muermann, Alexander, 2016. "Asymmetric information in automobile insurance: Evidence from driving behavior," CFS Working Paper Series 543, Center for Financial Studies (CFS).
    13. van Soest, Arthur & Hurd, Michael, 2008. "A Test for Anchoring and Yea-Saying in Experimental Consumption Data," Journal of the American Statistical Association, American Statistical Association, pages 126-136.
    14. David Aadland & Arthur Caplan & Owen Phillips, 2007. "A Bayesian examination of information and uncertainty in contingent valuation," Journal of Risk and Uncertainty, Springer, vol. 35(2), pages 149-178, October.
    15. Monika Bütler, 2002. "Flexibility and Redistribution in Old Age Insurance," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 138(IV), pages 427-437, December.
    16. Arthur van Soest & Michael Hurd, 2004. "Models for Anchoring and Acquiescence Bias in Consumption Data," NBER Working Papers 10461, National Bureau of Economic Research, Inc.
    17. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    18. Tetsuo Yamamori & Kazuyuki Iwata, 2023. "Wage claim detracts reciprocity in labor relations: experimental study of gift exchange games," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 573-597, July.
    19. Short, Jeremy C. & Palmer, Timothy B., 2003. "Organizational performance referents: An empirical examination of their content and influences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 90(2), pages 209-224, March.
    20. Shalvi, Shaul & Dana, Jason & Handgraaf, Michel J.J. & De Dreu, Carsten K.W., 2011. "Justified ethicality: Observing desired counterfactuals modifies ethical perceptions and behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 115(2), pages 181-190, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4012-:d:180056. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.