NEW TECHNOLOGIES FOR IMPROVING TOURISM STUDENTS TRAINING

Abstract

Tour operators should analyze consumers behavior in order to learn how to dig information on them, analysing the large amount of data found on Social Media Network. In fact, consumers broadcast their data on visual media platforms, both with their images and their comments, not only in their daily lives, but especially when they are traveling and enjoying the cultural and environmental beauties and services of the place that is hosting them (Cha et al., 2009; Giglio et al., 2015). This way of narrating their voyage can be analysed in order to understand what tourists want and do not, in order to better design appealing tours. To analyze the behavior of tourists, a short course on data analysis technologies was provided to students of the Tourism Sciences degree, at the University of Calabria. In particular, students learned to acquire data (images and text) from Twitter and Facebook, to analyze the text with sentiment analysis function, to analyze the images, identifying the faces (Lo et al., 2011). Furthermore, emotional expressions of the subjects have been categorized through the use of a simple Machine Learning (Bertacchini et al., 2017). Mathematica software provided the environment for the implementation of the tourists data analysis. By using a series of short programming tools, ad hoc developed, students have been able to extract data from the Social Media Networks, analysing and visualizing touristic data. For each tourist, they have been able to identify the visited points of cultural and touristic interest, mapped on the pictures and textual data posted on the Internet (Asakura, Iryob, 2007). From a questionnaire administered at the end of the course, we found that the training on new technologies was appreciated, above all for its potentialities in better modelling the tourist operator professional activities, adapting to new and technological requirements of contemporary consumers. This first pilot experimentation, although still completely empirical and not based on an experimental plan, has provided us with important results achieved by students. In particular, students achieved: a. a greater motivation to the study of tourist behavior; b. a greater awareness of data analysis technologies; c. a strengthening of their knowledge; d. an analysis of what tourists like and what they hate; e. new technological scenarios in which organize tourist routes based on the analysis of the obtained data. From an educational point of view, the implemented short training could help student to greater expand their creativity by using Big Data and Data analytics, two of the main technologies of the Industry 4.0 framework, thus giving them new opportunity to improve their skills and their future work.

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