Abstract:

We live in an era where higher education programs are submerged in a technologic environment, not only from a pedagogic perspective, but also in the administrative background where decisions are taken for the management of the entire academic structure. Information on the internet is large and it is impossible to retrieve it manually from the web. In this context, internet social networks (ISN) can offer support as an information tool, they are available at all times and it is possible to access them from practically any place. Besides, it can be guessed that students, teachers and administrators in all educational institution levels have an account on at least one ISN. Facebook and Twitter being the most popular and of bigger growth projected ones.

Due to the first of those being largely accepted as a communicative and collaborative platform, it has become the most used one in academic labor for its great amount of services offered: text, audio and video with unoppressive limits in the interchange or sharing between users. By the above, this project’s general objective is: to generate useful and pertinent information from data extracted from Internet social networks for decision taking in educational institutions.

The secondary objectives are:
(1) to evaluate the Gephi, NodeXL, LikeAnalyzer and Wolfram Alpha software and determine which one is the best to utilize in this research;
(2) to establish Facebook data search parameters;
(3) to search and extract data from Facebook,
(4) to select the extracted data for the analysis;
(5) to analyze the extracted data;
(6) to generate information from the extracted data.

The methodology proposed in this research is of the descriptive type since its purpose is to identify important characteristics of a sample for its analysis; as well as their personal and academic information and trend. The research focus is quantitative. The Autonomous University of Sinaloa (UAS) in Mexico was taken as a sample to extract its students’ and teachers’ Facebook data with the NodeXL software; a multivariate data analysis was later carried out to select relevant data and generate useful information for decision taking. As an important result, it was found that 62% of the students tend to prefer degrees in the technology and computers area, about 85% express a disliking toward mathematics related subjects, 40% believe to be studying the wrong degree. As for the teachers, 89% state they are tired because of work and 95% consider their labor to not be adequately paid.

Keywords:

Data analysis, data extracted, social networks, data mining, rational decision making.