No abstract

Low class attendance by university students is one of the factors that may be related to abandonment ratios, which constitute a serious socio-economic issue. The aim of this work is to confront the influence of the time management capacity of first-year students with their class attendance. With a factorial analysis of a survey carried out, four factors emerged: the students' perception of how they manage their time, the time they spend on less productive tasks, the ability to finish tasks on time and the use of time management tools. Moreover, with classification trees it was seen that students who are able to finish the tasks on time, have a greater capacity for concentration and spend less time on trivial tasks, have higher class attendance. With these profiles identified is expected to guide them to improve their time management, increase their class attendance and, as a consequence, decrease dropout rates

The tourism sector is experiencing a change in trend due to the widespread use of Web 2.0 by tourists. This phenomenon has prompted tourism agents to apply Big Data techniques and Business Intelligence tools to find out what tourists think. In this sense, the analysis of the Electronic Word Of Mouth in Social Networks is particularly relevant, and the academic literature has faced some difficulties in extracting data, determining its meaning and linking it to the destination. The aim of this work is to is to present the results that can be generated by the design of a tourism reputation index, through the analysis of data created collaboratively on the Twitter Social Network. For this purpose, a BI tool has been created to extract data from short text messages posted by users (tweets) and process them by implementing an Extract, Transform and Load process. In this process, Natural Language Processing is carried out, focusing on Sentiment Analysis tasks, applying an automatic classification model (Multinomial Naive Bayes) and then an automatic thematic categorisation of these texts depending on the words they contain. Subsequently, the data is analysed, calculating a global online reputation index based on sub-indexes of perception on different tourism aspects. These ratings are shown to users through an interactive dashboard, which allows comparison between different queries associated with a tourist location and a temporal period. This research helps tourism agents to better understand the public perception about destinations and make appropriate and well-informed decisions in real time, facilitating the intelligent management of data that enables the creation of smart tourism destinations with competitive, unique and sustainable advantages.

No abstract

No abstract