During the last decade, the use of digital technologies has experimented an accelerated penetration among youngsters, producing a wide variety of purposes of use. Today it is possible to see university students very engaged with digital devices, having a close relationship with equipment, software and Internet services (Boyd, 2014; Tapscott, 2009). There are several taxonomies that classify what young people do with these digital tools (Blank & Groselj, 2014; Carretero et al., 2017). The most frequent purposes of digital technologies use, among university students pursuing a degree in education, are Social (using social network to interact digitally), Entertainment (using streaming or interacting services to have fun) and Academic (using databases or educational content providers websites to gain a deeper understanding of educational content).

The academic use of digital technologies can be organized in three major categories: Search for information, Communication and Content creation (Cerda et al., 2018). Search for information is used by students to gain a deeper understanding of content covered during classes, which is considered necessary or relevant by learners. This process is carried out with search engines or by reviewing specific websites.  Communication is widely used by university students to collaborate among them, mainly through synchronic communication by using chat, or to a lesser extent, by email, in order to develop academic assignments. Communication also occurs between instructors and students, mainly to cover specific doubts. The third type of use, Content creation, relates mainly to preparing course materials for teaching and course activities, such as digital presentations, text reports or any other type of product related to teaching. A student teacher, who is well prepared in these academic purposes of use, can take good advantage of the multiple benefits that digital resources have to offer (Redecker, 2017).

It is essential for student teachers to be able to identify the level of digital technology use due the relevant role that they will play as educators in the near future (Cerda & Saiz, 2018). Being able to search for information, communicate with others and create digital content with an academic purpose not only reflects the students’ level of acquisition of digital competences (Gudmundsdottir & Hatlevik, 2017), but also it reflects their level of personal digital capital (Park, 2017).

Although the relationship between young people and digital technologies has been always under scrutiny in terms of immersion, level of dependency of digital resources and privacy of the information generated, the study of the relationship between digital technologies and its academic use has not kept accordance to its importance. One of the main limitations to understand this phenomenon is the lack of instruments to measure the different purposes of use displayed by student teachers. This fact is also reinforced by the lack of solid taxonomies in the field of education, able to define with accuracy, the object intended to be measured. Thus, it is a very relevant and necessary endeavor to get a deeper understanding about the way that university students, especially those pursuing a degree in education, use digital technologies.

Based on the above mentioned information, the aim of this study was to analyze the construct validity and reliability of the Scale of Purpose of Use of Digital Technologies in Chilean student teachers. The scale considered three factors: Social, Entertainment and Academic, and three academic subscales: Search for information, Communication, Content creation. Specifically, and based on the factorial structure and the invariance of the scale, it was also possible to analyze sex and course level differences among participants, for each of the different purpose-factors.

The participants in this study were 582 student teachers (275 women and 307 men) from a university located in Southern Chile. They were selected by convenience sampling and they account for 59% of the school’s student teacher population. In order to measure the different purpose of use, the research team developed a Likert type scale of 26 items (assessment scale from 1= Never to 10 = Always). The instrument measured three main factors: Social (e.g., “Use a social network to share information of my interest.”), Entertainment (e.g., “Play online game with others.”) and Academic in three subscales: Search for information (e.g., “Find information on the web for academic purposes.”), Communication (e.g., “Collaborate over the Internet with my classmates in order to study a topic.”) and Content creation (e.g., “Create or edit documents related with my studies in a word processor.”). The participants answered the scale and a questionnaire with demographic data about their program, year of enrollment, age and sex. Data were collected in the context of class (the students answered on paper). The participants read and signed a consent form approved by the University’s Ethic Committee. The collected data were transferred to a spreadsheet and later exported to two statistical software packages for their analysis. Data analysis was organized in five steps. First, data were explored to verify its correct input. Second, six missing answers had to be imputed by the average value obtained for the participants in the theoretical factor associated with the item. Third, with the available data, confirmatory factor analyses were carried out in order to verify the theoretical structure proposed (Kline, 2011). The following indexes of goodness of fit were calculated: X2, RMSEA, CFI, TLI, SRMR, and their respective index levels were found satisfactory considering the values recommended by Abad, Díaz, Gil, y García (2011): [CFI and TLI >0.90; RMSEA < 0.06 and SRMR < 0.08]. Fourth, after the construct validity was established, analysis reliability was calculated by using Cronbach’s Alpha coefficient. Fifth, variables for each factor were created in order to compare the levels of use by sex and by each year level, along the students’ five-year permanence in the program.

The CFA showed three items with low loading value in its subscale: “Use email for writing to family and friends” and “Chat with my friends about everyday activities” in Social factor, and “Play online with other people” in the Entertainment factor. After dropping those items, the analysis showed good indexes of fit for the general model: CFI= 0.910, TLI= 0.896, RMSEA= 0.051 SRMR = 0.061. Covariances between factors were: Social and Entertainment (.058), Social and Academic (0.36), Academic and Entertainment (0.23). Covariances among Academic factor and its subscales were: Search for information (0.81), Communication (0.86) and Content creation (0.87). Reliability of each scale was: Social (0.701), Entertainment (0.700), Academic (0.827), Search for information (0.858), Communication (0.805) and Content creation (0.758). Factor comparison by sex shows that in general Academic use, women outperforms men (M= 5.20, SD= 1.29; M=4.90, SD=1.30), t(580) =-2.84, p= .005. Same situation occurs in Search for information: women (M= 6.49, SD= 1.68; M= 6.17, SD= 1.49) t(580) =-2.46, p= .014, and Communication (M= 5.73, DS= 1.56; M= 5.73, SD=1.56) t(580) =-4.24, p= <.000. Men only outperforms women in Entertainment (M= 6.02, SD= 1.66; M= 5.66, SD= 1.75) t(580) =2.54, p= .011. The type of use through the year in the program shows the same tendency through the all years: Entertainment (M= 5.85), Academic (M=5.04) and Social (M= 4.71). The analysis of Academic subscales shows a higher cores Search for information (M=6.32), followed by Communication (M= 5.44) and Content creation (M=3.54). This study shows preliminary evidence about the construct validity and reliability of an instrument developed to measure different types of technology use among student teacher. Sex differences and the amount of use through the years open an interesting debate about how teacher training is shaping the way technology is used in future educators.

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