Study on distributed cognition processes and participation in collaborative construction activities in digital media

摘要

This research presents a work in progress in which we propose a mapping of digital experiences through the analysis of the behavior of a group of users from the social site Facebook with regard to the self perception of the interactive processes provided by some tools made available in this digital environment. We believe that the user of social networks, even when only recognizing the movements of their production process, access and information sharing reveals initial connection structures that act as mediating agents in the construction of social representations. Thus, the focus of interest is to observe the characteristics and properties conferred to the technological artifacts highlighting the correlation between the processes of distributed cognition and the participation in activities of collaborative construction in digital medium. This research assumed an exploratory nature, being the method employed essentially quantitative from the use of a structured questionnaire to perform the data collection organized by three sessions: a) characterization of the respondents; b) indication of the number of social networks that it is part of; c) evaluation scale composed of 15 variables on various aspects of interaction in the social network Facebook. The instrument was answered from a Likert scale of '5' (five) points, with values between '0' (zero) and '4' (four), zero being 'totally disagree' and '4' (four) "I totally agree". The questionnaire was applied on a university campus during the month of October 2017, with 10.79% of students enrolled in the institution responding to the instrument. The data obtained from the analyzes of the assertions of the questionnaire were examined by the SPSS software, through which the Principal Component Analysis (PCA) was used a multivariate statistical technique that consists in reducing a number of original variables in components not related to priori, aiming at reducing data to facilitate its interpretation.