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ABSTRACT

The objective of this study was to identify university student profiles with different levels of predisposition and usage of digital competences in social communication and collaborative learning (CSCCL) as well as technology use in information search and treatment (CSTI). The sample comprised 383 students from three state universities in Spain. The study employed a questionnaire called “basic digital competences 2.0 in university students” (COBADI). Chi-squared automatic interaction detection (CHAID) algorithm was used for data analysis due to its capability to handle both quantitative and qualitative variables, enabling profiling and the generation of predictive models with easily interpretable graphical representations (decision trees). The results revealed a high level of digital competence in socialization and execution of tasks online, managing digital tools for planning study time, and using resources for information searching and browsing. These findings align with previous works on collaborative writing on the Internet and digital competence. However, students demonstrated low digital competence in data analysis processes and image production using social software apps, which has been linked to task complexity and heavy workload in other studies. Interestingly, the students’ sociodemographic characteristics (age, sex, and university attended) did not influence their predisposition towards the analyzed digital competences. In conclusion, enhancing effective digital teaching in higher education can be achieved by incorporating the teaching of critical analysis of information, addressing information overload, providing instruction on social software apps, and emphasizing collaborative learning. These strategies aim to help students acquire and apply knowledge relevant to the current job market.

Keywords: digital competence, university, collaborative learning, social communication, ICT, CHAID algorithm