Engaging Students to Learn Forecasting Methods

Abstract

Presently, higher education institutions are faced with the challenge of developing student's abilities and skills needed in their future workplace. In what concerns technological skills, the use of spreadsheets for calculations, analysis of data and forecasting is a common and important practice in companies. Particularly, the MS Excel software is widely used by professionals from all fields. In this sense, Economic, Business and Marketing graduates need competencies in forecasting methods, extremely useful for decision-making processes. It thus becomes imperative to implement pedagogical practices to encourage students to use these technological tools. The later will promote the development of competencies in forecasting methods to solve future real problems. In this paper, we attempt to address these issues by analysing the MS Excel software capabilities as a teaching tool in a forecasting methods course. It was proposed to the students to carry out a learning project involving statistical concepts, namely linear regression, performed in MS Excel. We examine the performance and engagement of two samples of students with different backgrounds and from distinct realities. One group is composed of ERASMUS' students from several nationalities and fields of study and other group consists of Portuguese students of the Marketing Bachelor degree, both enrolled in an optional course of the bachelor degree in Marketing taught at Porto Accounting and Business School from Polytechnic of Porto. The effectiveness of this approach is shown through the analysis of results of students' projects. We verify that students in both groups achieved the task proposed goals and applied appropriately the required concepts in an engaged way.

The effect of challenge-based gamification on learning: An experiment in the context of statistics education

“…Despite the growing popularity of and need for forecasting skills, business schools slowly address this demand, and they generally disregard the need for increasing student motivation (Debnath et al, 2007). Business forecasting or statistical forecasting methods are usually considered complicated (Albritton and McMullen, 2006;Craighead, 2004;Gardner, 2008;Snider and Eliasson, 2013;Torres et al, 2018), making it difficult for students to remain motivated (Craighead, 2004). Taking this into account, Chu (2007); Donihue (1995); Loomis and Cox (2000); Loomis and Cox Jr (2003); McEwen (1994) suggest alternative teaching guidelines such as the use of a software or new technology in combination with real data and forecasting problems.…”

“…Consequently, both statistics and forecasting techniques are of vital importance in the economics curriculum (Loomis and Cox Jr, 2003) and in other fields such as business (Makridakis et al, 2008) or social problems, where data may help to make better decisions. However, often forecasting courses are not even offered as an independent course in business schools (Hanke, 1989) and when they are, students are discouraged to participate in the courses because they find the topic too complicated (Albritton and McMullen, 2006;Gardner, 2008;Snider and Eliasson, 2013;Torres et al, 2018) and demanding (Craighead, 2004). Therefore, regarding the education and especially the education in the field of statistics and forecasting, student motivation is crucial for their participation and understanding in order to reach their learning potential, meet business needs and get insights of the data to support the decision-making process.…”