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Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System
Birmingham Business School, University of Birmingham, United Kingdom.
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand.
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand.
Faculty of Business and Law, Anglia Ruskin University, Cambridge, United Kingdom.
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2024 (English)In: Forecasting, ISSN 2571-9394, Vol. 6, no 1, p. 204-223Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 6, no 1, p. 204-223
Keywords [en]
business forecasting, forecasting education, forecasting support systems, intelligent tutoring systems, time series decomposition
National Category
Information Systems Information Systems, Social aspects Educational Sciences Embedded Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-23694DOI: 10.3390/forecast6010012ISI: 001191583200001Scopus ID: 2-s2.0-85188792714OAI: oai:DiVA.org:his-23694DiVA, id: diva2:1848765
Note

CC BY 4.0 DEED

© 2024 by the authors.

Correspondence Address: D. Barrow; Birmingham Business School, University of Birmingham, University House, Birmingham, 116 Edgbaston Park Rd, B15 2TY, United Kingdom; email: d.k.barrow@bham.ac.uk

This research was funded by Coventry University Pump Prime Research Grant Scheme 2015.

Available from: 2024-04-04 Created: 2024-04-04 Last updated: 2025-02-18Bibliographically approved

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Kourentzes, Nikolaos

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