Since the permanent development of digital technologies and its resulting access on the population, the relationship between young people and these resources is every day closer (Boyd, 2014). Nowadays, young people use digital technologies with different purposes, which vary depending on the development of new applications and the demands typical of the context in which users get involved. Additionally, digital technologies increase the possibilities of actions never thought before (e.g., online banking) and they always aim to facilitate users’ life. This phenomenon not only enables the development of different taxonomies about the way that people connect to these tools in terms of access and use (Blank & Groselj, 2014), but also enhances the need to explain why young people use them intensively. Even though, this topic is of great interest regarding the general youth population, its analysis is even more relevant regarding student teachers because their relationship with technologies could imply a proper subculture (Ayala-Pérez & Joo-Nagata, 2019).

It is possible to identify different purposes of digital technologies use among student teachers, such as academic, entertainment or social purposes (Cerda et al., 2018). Among this group, the academic use of digital technologies can constitute a valuable support resource for autonomous practices of teacher professional development (e.g., Bragg et al., 2021). Furthermore, in the context of a future professional performance, the suitable knowledge of these tools’ academic use can contribute to carry out better practices of curriculum integration concerning digital technologies, which can impact positively on students’ learning, whether directly to them or serving to the teacher as a pedagogical model to acquire knowledge through autonomous or collaborative learning practices with these tools.

Every purpose of use can be addressed through the use of digital competences (e.g., Browsing, searching and filtering information; Storing and retrieving information; Interacting through technologies; Sharing information and content; Developing content), which have showed a wide variability on their frequency of use. Although this topic is relevant, no studies so far report the explanation of this variability, for example, studies that identify different profiles based on frequency of use of digital competences and link these profiles with other relevant variables connected to the academic use of these technologies.

The first objective of this study was to identify, on student teachers, the existence of subgroups with different profiles based on frequency of digital competences’ academic use. The second objective was to characterize these subgroups in terms of three areas of individual differences: self-directed learning, academic tenacity and disposition to learn and teach. Having a better understanding of the relationship between some characteristics of student teachers and the use and knowledge of digital competences will allow to strengthen the role of personal variables, which are commonly not considered because it is incorrectly thought that young university students are homogenous digital natives (Lluna & Pedreira, 2017).

The participants in this study were 446 student teachers (181 men and 265 women) from two universities located in southern Chile. Along with demographic data, four questionnaires were applied: 1) five subscales taken from Scale of Purpose of Use and Digital Competences (SPUDC), developed by the research team, which measures purpose of academic use of five digital competences based on DigComp (Ferrari, 2013): Browsing, searching and filtering information (3 items); Storing and retrieving information (4 items); Interacting through technologies (3 items); Sharing information and content (3 items); Developing content (4 items). 2) The Chilean adaptation for student teachers (Cerda & Saiz, 2015) of the Scale of disposition to self-directed learning (Fisher et al., 2001), which measures Self-management (10 items), Self-control (6 items) and Desire to learn (5 items). 3) Scale of academic tenacity, which measures the factors of Consistency of academic interest (6 items) and Perseverance of academic effort (6 items). This instrument is and adaptation of the one developed by Duckworth (2007), which measures tenacity as a general construct, context-free. 4) Scale of Disposition to Learn and Teach, instrument developed by the research team, which measures Disposition to disciplinary learning (5 items), Disposition to teach (5 items) and Disposition to pedagogical learning (5 items). The instruments were organized sorting their position in three different ways. Due to the pandemic situation, the measurement was applied remotely through the QuestionPro platform. Seven messages with an informative video about the research project were sent via email in order to promote the participation. Furthermore, during the remote classes, professors collaborated on ask their students to participate on the study. Before the participants answered the instruments, they read an informed consent approved by the university ethics committee. The existence of subgroups with different profiles of academic use of digital competences was addressed through a hierarchical cluster analysis based on the scores of the five competencies. The squared euclidean distance, as a similarity measure, and the Ward method to build the cluster were used. Then a series of one-way ANOVA were performed to compare the emerging clusters in terms of the five scores of digital competencies and the personal variables.

The pseudo-F and Je(2)/Je(1) stopping rules suggested two clusters while the agglomerative coefficient and pseudo-T2 suggested four. The classification into two clusters showed, according to coefficient η2, between 13% and 41% of the digital competences variance, while the classification into four clusters showed between 40% and 62% of the variance. Thus, this final solution was considered. The comparison of clusters (ANOVA), according to the means of the five competences, showed significant F values. The post-hoc exploration showed that 26 (87%) of the 30 possible pair comparisons were significant. This solution revealed a cluster with one advanced profile (C1, n = 79, 17,7%), two with intermediate profiles (C2, n = 160, 35,9%; C3, n = 105, 23,5%) and finally another one with a lower profile (C4, n = 102, 22,9%). The members of C1 showed high means (i.e., M ≥ 4.0) of use of the five competences. Both groups with intermediate profiles showed moderately high means (i.e., 3,0 ≤ M ≤ 3,9) on Browsing, searching and filtering information; Storing and retrieving information; Developing content, but they differ on Interacting through technologies and Sharing information and content. The group C2 showed a high mean on Interacting through technologies while C3 showed it moderately high. C2 showed a moderately high mean on Sharing information and content while C3 showed that value moderately low. Finally, participants of group C4 showed moderately low means on all competencies but a moderately high mean on Interacting through technologies. The comparison of the four clusters according to personal variables showed that all ANOVA analyses were significant. The post-hoc analysis suggested that 34 (71%) of the 48 pair comparisons were significant and showed the following general pattern: C1 > C2, C3 y C4; C2 y C3 > C4; C2 = C3.

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