IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v6y2024i1p12-223d1353125.html
   My bibliography  Save this article

Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System

Author

Listed:
  • Devon Barrow

    (Birmingham Business School, University of Birmingham, University House, 116 Edgbaston Park Rd, Birmingham B15 2TY, UK)

  • Antonija Mitrovic

    (Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand)

  • Jay Holland

    (Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand)

  • Mohammad Ali

    (Faculty of Business and Law, Anglia Ruskin University, Cambridge CB1 1PT, UK)

  • Nikolaos Kourentzes

    (Institutionen för Informationsteknologi, Högskolan i Skövde, Högskolevägen, Box 408, 541 28 Skövde, Sweden)

Abstract

In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide one-on-one online computer-based learning support affording student modelling, adaptive pedagogical response, and performance tracking. This study provides a detailed description of the design and development of the first Forecasting Intelligent Tutoring System, aptly coined FITS, designed to assist students in developing an understanding of time series forecasting using classical time series decomposition. The system’s impact on learning is assessed through a pilot evaluation study, and its usefulness in understanding how students learn is illustrated through the exploration and statistical analysis of a small sample of student models. Practical reflections on the system’s development are also provided to better understand how such systems can facilitate and improve forecasting performance through training.

Suggested Citation

  • Devon Barrow & Antonija Mitrovic & Jay Holland & Mohammad Ali & Nikolaos Kourentzes, 2024. "Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System," Forecasting, MDPI, vol. 6(1), pages 1-20, March.
  • Handle: RePEc:gam:jforec:v:6:y:2024:i:1:p:12-223:d:1353125
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/6/1/12/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/6/1/12/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Harvey, Nigel & Bolger, Fergus, 1996. "Graphs versus tables: Effects of data presentation format on judgemental forecasting," International Journal of Forecasting, Elsevier, vol. 12(1), pages 119-137, March.
    2. Thomas G. Tape & Jerry Kripal & Robert S. Wigton, 1992. "Comparing Methods of Learning Clinical Prediction from Case Simulations," Medical Decision Making, , vol. 12(3), pages 213-221, August.
    3. Fischer, Ilan & Harvey, Nigel, 1999. "Combining forecasts: What information do judges need to outperform the simple average?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 227-246, July.
    4. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    5. Lawrence, Michael J. & Edmundson, Robert H. & O'Connor, Marcus J., 1985. "An examination of the accuracy of judgmental extrapolation of time series," International Journal of Forecasting, Elsevier, vol. 1(1), pages 25-35.
    Full references (including those not matched with items on IDEAS)

    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. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    2. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    3. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    4. De Baets, Shari & Harvey, Nigel, 2020. "Using judgment to select and adjust forecasts from statistical models," European Journal of Operational Research, Elsevier, vol. 284(3), pages 882-895.
    5. Harvey, Nigel & Harries, Clare, 2004. "Effects of judges' forecasting on their later combination of forecasts for the same outcomes," International Journal of Forecasting, Elsevier, vol. 20(3), pages 391-409.
    6. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    7. Cedric A. Lehmann & Christiane B. Haubitz & Andreas Fügener & Ulrich W. Thonemann, 2022. "The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3419-3434, September.
    8. Han, Weiwei & Wang, Xun & Petropoulos, Fotios & Wang, Jing, 2019. "Brain imaging and forecasting: Insights from judgmental model selection," Omega, Elsevier, vol. 87(C), pages 1-9.
    9. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    10. Thomson, Mary E. & Pollock, Andrew C. & Gönül, M. Sinan & Önkal, Dilek, 2013. "Effects of trend strength and direction on performance and consistency in judgmental exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 337-353.
    11. Theocharis, Zoe & Harvey, Nigel, 2016. "Order effects in judgmental forecasting," International Journal of Forecasting, Elsevier, vol. 32(1), pages 44-60.
    12. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
    13. Harvey, Nigel & Harries, Clare & Fischer, Ilan, 2000. "Using Advice and Assessing Its Quality," Organizational Behavior and Human Decision Processes, Elsevier, vol. 81(2), pages 252-273, March.
    14. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    15. Petropoulos, Fotios & Goodwin, Paul & Fildes, Robert, 2017. "Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge," International Journal of Forecasting, Elsevier, vol. 33(1), pages 314-324.
    16. Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris, 2022. "Forecasting for social good," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1245-1257.
    17. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    18. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    19. Ramanathan, Usha & Muyldermans, Luc, 2010. "Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK," International Journal of Production Economics, Elsevier, vol. 128(2), pages 538-545, December.
    20. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.

    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:jforec:v:6:y:2024:i:1:p:12-223:d:1353125. 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.