ENGAGING STUDENTS TO LEARN FORECASTING METHODS
1 Polytechnic of Porto, ISCAP and CEOS.PP (PORTUGAL)
2 Polytechnic of Porto / ISEP (PORTUGAL)
About this paper:
Conference name: 10th International Conference on Education and New Learning Technologies
Dates: 2-4 July, 2018
Location: Palma, Spain
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.
Keywords: Higher education, Excel, forecasting, linear regression.