COMPUTER PROGRAMMING TO SUPPORT PROBABILITY EDUCATION: AN EMPIRICAL APPROACH

In the Italian school the teaching of probability has been relegated to a secondary and often marginal level, which has led in time to a number of misconceptions with potentially dangerous consequences, especially when one takes into consideration the strong growth of gambling.

On the other hand, specialized publications do nothing but feed these false probabilistic theories pushing the gaming business to higher levels. The majority of people do not understand that for a random phenomenon to come true the conditions surrounding the phenomenon are just as important as the phenomenon itself. This kind of misconception is one of the principal reasons that encourage gambling (Villani, et al., 2012).

As far back as 1979 Giovanni Prodi said:
For a long time now I have been convinced that a sound mathematical education for the young cannot be obtained without exploiting the great conceptual and heuristic wealth of probability and statistics … meant … as a reflection on some fundamental knowledge processes and not just as a fundamental instrument for experimental and human science.

This paper fits the theoretical framework outlined so far, and presents a design-based research methodology relating to an empirical approach to probability in middle school.

The author’s intention is to promote the use of current information technology tools to support probabilistic reasoning, and to show how existing pre-packaged didactic software only allow the rigid application of a series of ‘recipies’ in limited contexts, and are not easily adapted to models taken from real situations. In this pedagogical perspective the computer is used as a programming tool within the MatCos learning environment (Costabile, & Serpe, 2013).

The choice of presenting some random phenomena through programming experiences stems from the need to suggest alternative methods to traditional teaching. In most cases in schools, the study of probability is approached as a guide to properties and results to be applied in contexts often restricted, a methodology which is insufficient to understand the true meaning of probability (Frassia, 2015).