Sociodigital practices, competences, mindsets, and profiles of Finnish students before and after the COVID-19 distance learning period

Abstract

Digital fluency is a central 21st-century competence. Schools are responsible for ensuring that all students cultivate sophisticated sociodigital competences and mindsets needed for studying and collaborating through and around technology and overcoming digital challenges encountered. Although some schools have successfully integrated digital technologies into traditional schoolwork, students are not provided sufficient structured training in creative and academic practices of using digital technologies. This study explored changes before and after the COVID-19 pandemic in Finnish primary and middle school students’ perceived sociodigital study practices, competences, mindsets, and profiles. Participants were asked to respond to the Sociodigital Practices Inventory (SDPi), which assessed their sociodigital study practices, competences, and mindset. The participants consisted of 947 cohort 1 students (5th grade in 2019 and 6th grade in 2020) and 771 cohort 2 students (7th grade in 2019 and 8th grade in 2020). The results revealed subtle changes in students’ perceptions regarding schools’ digital practices; primary school students experienced an increase in basic practices while middle schoolers experienced a decrease in perceived advanced practices. Both boys’ and girls’ self-evaluated academic sociodigital competences increased from 2019 to 2020, while their artistic and technical competences decreased. Primary school boys’ sociodigital mindsets increased, while that of middle school girls decreased. The analyses revealed four latent profiles of digital fluency: Inexperienced, Enthusiastic, Humble, and Driven. We propose that it is vital to build a multidimensional view of students’ digital fluency by exploring interrelations between their sociodigital practices, competences, mindsets, and profiles.

Keywords:

• Digital fluency
• sociodigital practices
• sociodigital competence
• sociodigital mindset
• sociodigital profiles
• COVID-19

Reviewing Editor:

• Muesser Nat, Management Information Systems, Cyprus International University, Turkey

Subjects:

• Information & Communication Technology (ICT)
• Classroom Practice
• Primary/Elementary Education

Introduction

Rapid societal changes and global societal crises, such as climate change and the COVID-19 pandemic, have challenged prevailing educational practices. The emerging innovation-driven society requires the use of sophisticated digital technologies to solve nonroutine problems and produce novelty and innovation in teams and networks. To meet these societal challenges, profound educational transformations are needed in terms of using digital technologies to foster social-collaborative and creative practices of learning and instruction. Such transformative efforts are aligned with 21st-century skills, which highlight the combination of creativity and innovation, communication and problem-solving skills, and socioemotional learning competences with digital fluency (Binkley et al., 2012; van Laar et al., 2017).

In the present study, we characterized the instruments of young people’s multifaceted social and digital practices as ‘sociodigital technologies’ (e.g. Blikstein, 2013; Ito et al., 2009; 2019; Kafai & Peppler, 2011; Rheingold, 2012). These are socially defined, mediated, and integrated digital tools, platforms, applications, and software, including digital services, such as social media, media production tools, videogames, educational technology, extended Internet, programming, robotics, microcomputer-based labs, and modeling tools. The uses of such technologies are anchored in social interactions, collaborative activities, and collective knowledge production mediated by digital technologies and social networks (Hakkarainen et al., 2015). Discussing sociodigital technology is justifiable because practically all digital activities are already social in nature and shareable with fellow participants. Furthermore, there is no need to make a distinction between software and hardware in this context, because they all enable comparable and intertwined sociodigital practices.

Students develop sociodigital competences across both informal and formal contexts. Ubiquitous mobile and wireless technologies enable new cohorts of students to be in constant interconnection with their peers (Anderson & Jiang, 2018; Rideout et al., 2010). Simultaneously, their patterns of using digital technologies are heterogeneous and often rather shallow. As not all students have access to the resources and social support required for achieving creative participation and reaching advanced levels of digital competence, researchers are concerned about the creative participation gap (Jenkins et al., 2009; Jenkins & Ito, 2015) and innovation inequity (Barron, 2004; Barron et al., 2014). Investigations have also revealed substantial gender differences in self-assessed digital competence (Van Dijk, 2020), and digital self-efficacy (Downes & Looker, 2011; Papastergiou, 2008; Tømte & Hatlevik, 2011). Although the situation might be changing and gender differences are becoming less prevalent, girls and boys still have different relations to technology.

Despite the fact that some schools have successfully integrated digital technologies with traditional schoolwork, the educational use of such technologies often remains relatively superficial, focusing mainly on basic digital skills and the completion of reproductive assignments. Although students may have developed impressive informal sociodigital competences, they do not have sufficient structural support for the academic and creative use of digital technology (Hakkarainen et al., 2015). To learn advanced technology use, young people need the support of knowledgeable peers, teachers, parents, and other adults (Barron et al., 2009, 2014). The COVID-19 pandemic forced schools to move to distance or hybrid practices of learning and instruction, regardless of the highly varied sociodigital competences of students and teachers. Although many schools had worked to integrate digital practices into their pedagogic and operational cultures, the pandemic radically changed the situation, forcing schools to do a ‘digital leap’ in terms of moving—within a few days—to either total distance education or hybrid forms of learning and teaching (Karvi, 2020).

Arguably, digital fluency is a central 21st-century skill (Barron et al., 2010, 2014), and schools are responsible for ensuring that all students, both boys and girls, have the capabilities to overcome digital challenges and study and collaborate through and around technology (Hakkarainen et al., 2015). Digital fluency requires a sociodigital mindset, both in terms of having experience-based confidence in one’s capabilities of using digital technologies and active engagement in solving digitally mediated challenges and tasks. This was critical, especially during the global pandemic, which disturbed the traditional practices and methods of education and required extended periods of distance learning. The present study involves an original combination of studying changes in Finnish students’ digital fluency through their sociodigital competences, sociodigital mindsets, and the perceived digital practices of schools between 2019 and 2020. Tracing the latent sociodigital participation profiles that the present students relied on when negotiating the challenges of schoolwork in the context of the COVID-19 pandemic between 2019 and 2020 makes this study unique.

Sociodigital competences, mindsets, and profiles of students before COVID-19

Sociodigital competence

Diverse everyday sociodigital practices provide versatile learning opportunities (Eynon & Malmberg, 2011), which enable young people to participate in developing their digital competences both in and out of school. The academic and creative competences involved in using sociodigital technologies may be fostered through knowledge building (Scardamalia & Bereiter, 2022), the educational maker movement (Blikstein, 2013; Kafai & Peppler, 2011; Keune & Peppler, 2019; Rouse & Gillespie Rouse, 2020), and connected learning (Ito et al., 2013, 2019; Peppler et al., 2022), all of which emphasize learning through collaborative inquiry and the creation of artifacts and knowledge. Such practices and projects are often applied across science, technology, engineering, art, and math (STEAM, Conde et al., 2021), or they can be part of innovation education (Gunnarsdottir, 2013; Korhonen et al., 2022a; Licht et al., 2017) aimed at fostering students’ creativity, critical thinking, and interest in related disciplines. Students’ sociodigital competences can be expanded by integrating knowledge from multiple sources, remixing multiple modalities, and trying out previously unfamiliar digital tools (Binkley et al., 2012). The intensity and complexity of students’ technology-mediated participation advance their sociodigital competences (Eynon & Malmberg, 2011; Hietajärvi et al., 2016). In particular, the interrelations between sociodigital actions and specific competences are mediated by the quality of technology-mediated practices (Ferrari, 2012).

According to Barron et al. (2010, 2014), young people cultivate their digital fluency through the creative and academic use of sociodigital technologies. Digital fluency involves the capabilities of creative expression, creating and building knowledge, engaging in discourse interaction, and peer collaboration. Ito et al. (2009, 2019; Peppler et al., 2022) highlighted the importance of creative production, which involves writing, visual expression, media production, fabrication, and peer collaboration.

The capability to engage in creative sociodigital participation, such as seeking, building, sharing and critically evaluating knowledge can affect students’ success at school and work (Scardamalia & Bereiter, 2022; Takeuchi, 2012). Kafai and Peppler (2011) defined participatory competence as related to the technical, critical, creative, and ethical practices of media production relevant for participating in do-it-yourself culture. Technical practices consist of solving technical problems, using graphical applications, remixing media content, applying computational thinking, and coding. Critical aspects, in turn, involve assessing media, mastering referencing, and understanding interpretative aspects of media texts. Furthermore, the creative forms of media practices involve considering the aesthetic and artistic aspects of media production. Finally, ethical practices guide the process of crediting personal ownership together with referencing remixing peer-created content (Kafai & Peppler, 2011).

Hietajärvi et al. (2015) measured primary and lower secondary school students’ self-reported digital competences with 14 items representing two separate skillsets: six items assessing basic skills (e.g. ‘How competent do you see yourself in editing text documents?’) and nine items assessing advanced skills (e.g. ‘How competent do you see yourself in programming?’). The sum variables were constructed by calculating the average value of each item in each construct. The authors found a significant gender effect in terms of boys reporting more advanced skills than girls (Hietajärvi et al., 2015).

Today, digital competences are becoming critical in productive societal participation, and, as such, investigators have become more concerned about digital divides (Van Dijk, 2020). The term ‘digital divide’ originally referred to unequal access to computers and the Internet with respect to parents’ socioeconomic status and level of education (Barron et al., 2010). The concept of the digital divide has been extended to include unequal access to the creative and academic use of sociodigital technologies (Hakkarainen et al., 2015; Pedró, 2012) and the accompanying differences in associated sociodigital competences (OECD, 2012). Although Western countries have practically universal access to the Internet, mere access to technology does not provide sufficient support for cultivating sophisticated digital competence nor facilitate in-depth technology-mediated learning (Jenkins & Ito, 2015). Highly educated parents are able to provide more sophisticated technologies, more intensive and refined assistance, and richer learning resources for their children than their less educated counterparts (Barron et al., 2009, 2010, 2014; OECD, 2012). Barron (2004) conceptualized this insight as ‘innovation equity’, which refers to unequal opportunities to develop the sociodigital competences needed to create, invent, or actively influence one’s personal and social lives. Jenkins et al. (2009) Jenkins & Ito (2015) characterized the creative participation gap as ‘unequal access to the opportunities, experiences, skills, and knowledge’ (2009) needed to prepare young people for full global participation in the future.

Sociodigital mindset

Beyond actual skills and competence in using sociodigital technologies, young people’s orientation toward the use of sociodigital technologies also matters. Students who may not be skillful but willing to take up the challenges of learning sociodigital technologies are likely to acquire skills to deal productively with unforeseen challenges, such as those encountered during the COVID-19 pandemic. To capture this phenomenon, we propose a new concept called the ‘sociodigital mindset’, which is defined as experience-based confidence in one’s capabilities in overcoming technological obstacles and learning challenging digital practices (digital efficacy), as well as active engagement in using digital technologies and practices in one’s schoolwork (digital engagement). ‘Digital efficacy’ is a contextual form of self-efficacy (Bandura, 1997); it refers to an agent’s experience-based belief in and trust regarding their capabilities of overcoming digital challenges and obstacles and learning digital competences that initially appear completely out of reach. Digital efficacy is close to computer self-efficacy (Tømte & Hatlevik, 2011) and Internet self-efficacy (Eastin & LaRose, 2006; Kim & Glassman, 2013; Tsai et al., 2011) and is understood as the belief in one’s experienced capabilities in carrying out technology-mediated actions for meeting certain learning objectives. Digital self-efficacy is analytically differentiated from actual digital competence; it is a general disposition toward participating in solving progressively more challenging problems and learning capabilities related to digital technologies and practices.

Another aspect of a sociodigital mindset is digital engagement, i.e. willingness and motivation to use sociodigital technologies in schoolwork. Digital engagement entails that informally developed digital competences are expanded toward formal learning, and such expansion may be interpreted as indicating the interconnection of informal and formal spheres of learning (Ito et al., 2013, 2019; Jenkins & Ito, 2015; Peppler et al., 2022). Furthermore, digital engagement is an indication that a student is passionate about using digital technologies and, consequently, is highly motivated to participate in digitally mediated activities. Digital engagement indicates that a student wishes to participate in digital schoolwork, which can be interpreted as reflecting the gaps between educational practices and those of active sociodigital participants (Hietajärvi et al., 2020). Digital engagement makes students highly sensitive to the possibilities of using digital technologies at school. It also encourages them to highlight the digital aspects of their school activities, given that the availability of digital technologies may not be as conspicuous for less engaged students in the same environment. Finally, digital engagement may be related to students’ ways of identifying with digital practices, considering themselves to be individuals who enjoy doing schoolwork with digital technologies (Passey et al., 2018). A sociodigital mindset is critical for empowering students to work with difficult tasks and overcome challenges. Such a mindset also helps them learn and cultivate capabilities, which succeeding in the digitalizing work-life requires, such as using digital technologies for continuously adapting, learning, and innovating.

Sociodigital participation profiles

Various ways of clustering young people according to their sociodigital participation profiles have been proposed. For example, Rideout et al. (2010) identified heavy, moderate, and light media users according to the amount of time invested in consuming media content in informal contexts. Considering multitasking, heavy users aged 11- to 14-years-old spent almost 12 hours per day with one or another media platform. The results revealed that boys tended to spend more time with computers than girls, who apparently lost interest in computer games as teenagers (Rideout et al., 2010). Further, Eynon and Malmberg (2011) clustered students aged 8- to 19-years old according to their online communication, information seeking, entertainment, and participation. The less-skilled Internet users (‘peripherals’) were distinguished from a large group of average-level Internet users (‘normatives’), above-average Internet users (‘all-rounders’), and a small group of highly engaged Internet users (‘active participators’) (Eynon & Malmberg, 2011). The authors did not report gender differences.

Li et al. (2017) identified three latent profiles of adolescents’ sociodigital participation among lower secondary school students in Finland. The clustering was based on the frequency of such sociodigital activities as social media presence, action gaming, media composing, constructing personal knowledge, recreational gaming, social learning, and social gaming. The profiles were conceptualized as basic, gaming-oriented, and creative participators. Their results revealed that the ‘basic participators’ (76% women) emphasized social networking but were below average in engagement in all activities. ‘Gaming-oriented participators’ (82% men) played intensively social and action games, having below average intensity in other regards. Finally, the ‘creative participators’ (48% women, 52% men) showed above-average skills in all activities. The researchers also examined the relationships between the profiles, taking into account gender and the levels of self-reported advanced sociodigital competences among the participants. The investigations further revealed that the creative participators self-reported higher digital competences than those of basic or gaming-oriented participators. Moreover, male creative participators reported having higher competences than female participators (Li et al., 2017).

Sociodigital practices in finnish schools during the COVID-19 pandemic

The Finnish government closed schools from March 18th until May 13th, 2020, as a result of which schools were required to make the transition to distance teaching and learning (Government of Finland, 2020). However, 1st-, 2nd- and 3rd grade pupils were allowed to go to school. According to the guidelines of the Finnish National Agency for Education (FNAE, 2020), teaching and learning were expected to continue according to the compulsory school curricula during the distance teaching period. Simultaneously, they were organized in alternative ways, including the use of various digital learning environments and solutions and, when necessary, independent learning. When the second wave of the pandemic slowly became stronger in October 2020, the government allowed local and regional educational decisions to be applied to control the pandemic. Thus, during the autumn 2020 and spring 2021 terms, primary and middle schools were mostly open, yet some were closed fully or partly for one to three weeks, according to local pandemic situations.

According to the FNAE (2020), primary, lower, and upper secondary teachers changed their teaching to distance teaching rather easily. The relatively high levels of teachers’ and students’ basic digital competences and the quality of the digital infrastructure in Finnish society supported this change. Furthermore, school laptops were commonly loaned to students during the pandemic. One reason for the rather successful transition to distance teaching was the digital-tutor-teacher model implemented in 2017, which involved training digitally advanced teachers to support the digital-professional learning of their colleagues. A study on a representative sample of Finnish principals and teachers indicated that the rapid transition to the distance learning period went surprisingly well (Ahtiainen et al., 2020). However, the students experienced distance learning in different ways. On the one hand, some students estimated that distance learning suited them well and felt that learning at home was more effective than at school. On the other hand, one-third of primary school students estimated that they learned less than usual during the distance learning period. Although most teachers felt that their workloads were higher than in a normal situation, they also reported increased collegial collaboration and the development of digital skills across the distance teaching period. The challenges were most often related to students’ devices and teachers’ equipment and network connections. Furthermore, parents and guardians had to take more responsibility for their children’s learning than usual, and about half of them felt that this increased their stress levels.

The Finnish Education Evaluation Centre (Karvi, 2020) collected a representative random sample from 70 compulsory school principals and 185 primary teachers in May 2020. In that report, students in primary and middle schools identified various challenges during the distance learning period life management, such as making a personal schedule, learning difficulties or lack of support, and lack of opportunities or space to study at home. In addition, one-fifth of teachers estimated that students have had many challenges in planning their learning and engaging in independent learning. However, students reported that retrieving information independently, taking responsibility for their own learning, and establishing a schedule for their learning were engaging. According to the evaluations made by both teachers and students, there was a lack of support, especially among special needs students, and a minor lack of digital tools at students’ homes. There were especially challenges among students who were not native Finnish or Swedish speakers. The importance of cooperation between home and school was emphasized in all municipalities, especially for identifying the need for student support in compulsory education. However, the support offered to students varied among municipalities.

A study of Lavonen and Salmela-Aro (2022) reported that the distance learning period accelerated the development of teachers’ and students’ digital competences. The Karvi (2020) evaluation describes the scale of the development by using the term ‘digital leap’. Karvi’s evaluation provides good examples of how teachers began to prepare themselves and their students for the second wave of distance learning. The teachers explained how they used digital tasks in parallel with traditional teaching methods to facilitate a possible transition to distance learning in the spring or autumn 2021 term. However, Korhonen et al. (2021) reported a great deal of variation in running the school days and that students’ age, available tools and programs, teachers’ digipedagogical competences, and the specific circumstances of homes influenced the planning of structure and the implementation of school days during the distance learning period.

Research aims

Literature reviewed above highlights importance of providing students multifaceted possibilities for participating in developing their sociodigital competences in and out of schools (Barron et al., 2014; Eynon & Malmberg, 2011; Ito et al., 2019). In this regard, the extent to which students are provided structured opportunities for developing their digital skills at school plays a crucial role. Students’ academic and creative sociodigital competences can be developed, digital divides overcome, and digital fluency enhanced by engaging students in computer-supported collaborative learning (Stahl et al., 2022), building and creating knowledge (Hakkarainen et al., 2015; Scardamalia & Bereiter, 2022), learning by making (Blikstein, 2013; Kafai & Peppler, 2011; Keune & Peppler, 2019; Korhonen et al., 2022a; Rouse & Gillespie Rouse, 2020), and connected learning (Ito et al., 2013, 2019; Peppler et al., 2022). Beyond actual sociodigital competencies, students also have to cultivate sociodigital mindset, consisting of 1) digital efficacy (Eastin & LaRose, 2006; Kim & Glassman, 2013; Tømte & Hatlevik, 2011; Tsai et al., 2011) entailing experience-based trust in one’s capability of overcoming digital challenges and 2) digital engagement (Hietajärvi et al., 2020; Ito et al., 2013, 2019; Jenkins & Ito, 2015; Peppler et al., 2022) involved in enthusiastic application of informally developed digital practices for schoolwork. Moreover, various ways of clustering students’ sociodigital participation profiles have been proposed (Eynon & Malmberg, 2011; Li et al., 2017; Rideout et al., 2010) for deepening understanding of their informal and formal sociodigital practices. The present study aimed at comparing the changes in students’ perceptions related to their schools’ sociodigital practices as well as their sociodigital competences and mindsets before and after the COVID-19 distance learning period. Furthermore, we aimed to explore what kinds of sociodigital profiles can be found in students. The research questions were as follows:

1. How did students’ perceptions of schools’ sociodigital practices change from 2019 to 2020?

2. How did students’ sociodigital competences and sociodigital mindsets change from 2019 to 2020?

3. What kinds of sociodigital profiles can be found in Finnish students, and how did the profiles change from 2019 to 2020?

Methods

Research setting and participants

The present study was carried out through a researchpractice partnership (Penuel et al., 2020) with a team of researchers, city of Helsinki education administrators, and schools. The data collection was conducted using the novel Sociodigital Practices Inventory (SDPi) for young students, which was developed to measure sociodigital participation and capture the changing nature of sociodigital phenomena (Korhonen et al., 2020). A representative sample of schools was targeted in collaboration with the Helsinki City Education Division, thus ensuring participation across diverse districts, school sizes, and types of schools. Data were collected in fall 2019 and 2020 following the same students from the same schools of two age cohorts across the 5th and 6th grades (cohort 1, age: 11–12 years) and across the 7th and 8th grades (cohort 2, age: 13–14 years). In the first year of data gathering, there were 1262 cohort 1 participants and 1219 cohort 2 participants from 57 comprehensive schools in Helsinki. In the following year, the same sample was targeted, and there were 1242 cohort 1 participants and 1212 cohort 2 participants. Of these, 947 (61%) students in cohort 1 and 771 (46%) students in cohort 2 participated at both time points (see Table 1).

Table 1. Participating students from Helsinki region.

	Grade (Year)	Targeted students	Total participants	Response rate	Longitudinal participants	Longitudinal response rate	Gender
							Girls	Boys
Cohort 1	5^th (2019)	2852	1262	44 %	947	75 %	476 (50 %)	439 (46 %)
	6^th (2020)		1242	43 %
Cohort 2	7^th (2019)	3418	1219	36 %	771	63 %	396 (51 %)	336 (44 %)
	8^th (2020)		1212	35 %

Note. A small number of students (cohort 1 = 29, cohort 2 = 32) reported a different gender on the consecutive years, and these students’ gender information was classified as missing as we were not able to identify their gender.

Participation in the study was voluntary, and affirmatory consent forms were collected digitally from the students and their parents prior to data collection. The number of participants was satisfactory, and the response rate was 35%–44%. The gap between the targeted students and actual participants (Table 1) is due to unreturned parental consent forms (∼40%), unwillingness to participate (∼1%), students’ absence during data collection, and schools’ or teachers’ exhaustion or inability to include the data collection into their timeframe. The study protocol was approved by the University of Helsinki Ethical Review Board in the Humanities and Social Behavioral Sciences.

The data collection was led by teachers during school hours in accordance with the instructions supplied to schools, which was supported by video guidance for students to respond to the questionnaire. Students filled in an online self-report questionnaire using their mobile phones or laptops provided by their schools. The questionnaire was available in Finnish, Swedish, and English and took around 45 minutes to complete. Based on students’ and teachers’ feedback related to the questionnaire’s length, in 2020, the SDPi was apportioned into three shorter versions to mitigate students’ strain and reduce response time. We used item response theory (IRT; Samejima, 1969, 2016) to model sociodigital competences and practices as the basis for making the different question sets, which we further explain in the Data Analysis section.

Measures

Data collection was conducted using the novel SDPi for young students, which was developed to measure sociodigital participation and capture the changing nature of sociodigital phenomena (Korhonen et al., 2020). The inventory consisted of several different measures, but only relevant measures for this article were reported and analyzed in this study (see Korhonen et al., 2020 for all measures in the inventory).

Schools’ sociodigital practices

With respect to the digital practices of schools, we asked students about the frequency of using digital technology in schoolwork: ‘How often are the following things involved in your schoolwork?’ Students rated the statements on an intensity-of-activity scale ranging from 1 (never) to 7 (daily). They were also asked to rate statements related to basic digital practices in schoolwork, such as ‘In class, we practice basic use of digital technology (e.g. sharing a document, word processing, and using e-mail and the Internet)’ and about the use of advanced digital practices, such as ‘In class, we build devices utilizing automation, such as robots or smart devices’ (see Korhonen et al., 2020, Appendix Tables A1A and A1B for all items and factor loadings). The internal consistencies for basic digital practices with 6 items (a = .74–.77) and advanced digital practices with 7 items (a = .83–.84) were satisfactory.

Sociodigital competence and mindset

Students’ beliefs about their sociodigital competences and sociodigital mindsets were assessed in a self-report questionnaire. We used a measure of sociodigital competence, which was revised based on our earlier studies (e.g. Hietajärvi et al., 2016). They were asked ‘How well can you do the following things related to digital technology?’ and 15 items were rated on a digital fluency scale ranging from 1 (not at all) to 5 (very well). We asked students about their academic (e.g. ‘I can use word processing programs such as Word’), artistic (e.g. ‘I can edit videos (e.g. iMovie, VSDC, PowToon)’), and technical (e.g. ‘I can build automated devices, such as robots or smart products (e.g. Lego EV3, Micro:bit, and Arduino)’) sociodigital competence beliefs (see Appendix Tables A2A and A2B for all items and factor loadings). The internal consistencies were all satisfactory: academic competence with 6 items a = .77–.80, artistic competence with 5 items a = .82, and technical competence with 4 items a = .81–.82.

Students’ attitudes toward using digital technologies in their schoolwork, as well as how confident they felt about their own skills, were assessed using a measure on students’ sociodigital mindsets, which was revised based on Hietajärvi et al. (2020). They were asked, ‘How well do the following statements on digital technology describe you?’ and the items were answered on a 5-point Likert scale. The measure of the sociodigital mindset consists of two components: wish for digital schoolwork and digital self-efficacy. The wish for digital schoolwork consists of items such as ‘I am more enthusiastic about my schoolwork when I am allowed to use digital technology’, while digital self-efficacy consists of items such as ‘I am prepared to put in a lot of effort to learn something related to digital technology’ (see Appendix Tables A3A and A3B for all items and factor loadings). The internal consistencies were satisfactory: wish for digital schoolwork with 3 items a = .88–.91 and digital self-efficacy with 3 items a = .82–.84.

Data analysis

RStudio and R statistical programming languages were used to calculate IRT estimates for schools’ digital practices and students’ sociodigital competences. IBM SPSS Statistics 25 was used for data analysis regarding t-tests and statistical comparisons, and profile analyses were conducted using MPLUS 8.6 statistical software.

Based on students’ and teachers’ feedback related to the questionnaire’s length in 2019, the SDPi was apportioned into three shorter versions in 2020 to mitigate students’ strain and reduce response time. Three different versions of the questionnaires were formed with anchor questions. To identify the anchor questions for each measure, we used principal component analyses (PCA) with oblimin rotation (Appendix Tables A1–A3.). The anchor questions were included in all three questionnaire versions, and they enabled the combination of these versions in the analysis stage. Each questionnaire version was shortened by 25% from the original SDPi used in 2019. The reliabilities of each shortened measure in each questionnaire version were adequately similar and satisfactory (a = .61–.81), indicating the equivalency of the questionnaire versions. We used IRT (Samejima, 1969, 2016) to model sociodigital competences and practices for the different question sets.

In this article, IRT was applied to participants’ perceptions of digital practices at school and to the self-assessment of their sociodigital competences. First, IRT was applied as a graded model (Samejima, 1969, 2016) to calculate parameters for estimates from the 7th-grade sample (cohort 2, 2019), when all participants were asked to answer each item of the measures. These parameters were used as fixed calibration (Kang & Petersen, 2012) to form parameters for the new questions from the 8th-grade sample (cohort 2, 2020). The final parameters were then used as fixed to form person estimates from fifth (cohort 1, 2019) and sixth graders’ (cohort 1, 2020) questionnaires. In this way, we were able to estimate differences between the two separate samples (i.e. cohorts 1 and 2). The parameters for each participant were calculated using only their answers, meaning that missing information was not estimated.

However, the estimates calculated from the IRT analyses do show differences from regular Likert-scale values. For one, IRT estimates do not limit maximums or minimums. Estimates describe participants’ competences or opinions in relation to the whole measured sample. We rescaled our original midpoint of the IRT scale to 500 points, similar to the PISA assessments (OECD, 2019). We fixed one whole step in the IRT scale to 100 points in a new scale. This means that an average 7th-grade participant (participant in cohort 2, 2019) is estimated to gain 500 points. This is not equal to the average in the Likert scale, because every question has a unique distribution and effect for estimates. IRT provides an opportunity to estimate different questionnaire sets together in cases in which participants have different kinds of question sets. In Likert scales, a similar solution will lead to bias in averages based on the answered questions.

Our first and second research aims were to understand how students’ perceptions of their school’s digital practices and their self-evaluations of their sociodigital competences and sociodigital mindsets changed from 2019 to 2020. We first evaluated whether there were gender differences between boys and girls in our measures. As we found large and expected gender differences in our test variables, we decided to perform paired-samples t-tests for girls and boys separately (results of the independent samples t-tests regarding gender differences are available upon request from the authors). This step provides a more detailed understanding of the development and changes experienced by boys and girls, in contrast to investigating the changes regarding the whole sample. Cohen’s d was used to investigate the effect size.

Our third research aim was to identify the different types of sociodigital profiles that can be found among students in different years. Latent profile/class analysis (LPA/LCA; Williams & Kibowski, 2016; Vermunt & Magidson) was applied to three sociodigital competence factors together with two sociodigital mindset components. The number of profiles was determined by statistical numeracy (see Appendix Table A4) so that all samples would have the same number of profiles. The graphical solutions used in the decision-making process were subsequently interpreted in the Results section solutions (see Figures 1A and 1B under Sociodigital Profiles of Students).

Figure 1A. Graphical interpretation of four profile solution dimensions means in primary school. due to readable interpretation, scales are presented by Z scores.

Figure 1B. Graphical interpretation of four profile solution dimensions means in middle school. due to readable interpretation, scales are presented by Z scores.

Results

Students’ perceptions of their schools’ sociodigital practices from 2019 to 2020

To answer our first research question, we examined students’ perceptions of their schools’ sociodigital practices using a 13-item frequency measure. Table 2 displays students’ perceptions regarding basic and advanced digital school practices in both primary school (Grades 5–6) and middle school (Grades 7–8). On the one hand, students considered that basic digital practices were utilized more often than once a month, on average, with mean values ranging from 3.30 to 3.68 (see Table 2). All mean values fell below the midpoint of the scale, indicating that even basic digital practices are not commonly used in schools. The mean values of perceived advanced digital practices, on the other hand, were at the lower end of the scale, varying from 1.68 to 1.99, indicating that many students may not have had the chance to try these practices. Primary school students perceived both basic and advanced sociodigital practices to be used slightly more often than middle school students did.

Table 2. Means of cohort totals and paired-samples t-tests of girls’ and boys’ perceived sociodigital school practices.

	Total		Girls					Boys
	5^th grade M(SD)	6^th grade M(SD)	5^th grade M(SD)	6^th grade M(SD)	t	df	Cohen’s d	5^th grade M(SD)	6^th grade M(SD)	t	df	Cohen’s d
Basic	3.57 (1.21)	3.68 (1.17)	495.22 (90.73)	508.34 (81.09)	3.21***	474	0.15	504.64 (98.64)	512.85 (81.97)	1.73	430	n.a.
Advanced	1.99 (1.02)	1.91 (1.07)	504.01 (87.89)	507.35 (71.90)	0.79	471	n.a.	526.91 (104.52)	525.59 (74.30)	−0.26	429	n.a.
	7^th grade M(SD)	8^th grade M(SD)	7^th grade M(SD)	8^th grade M(SD)	t	df	Cohen’s d	7^th grade M(SD)	8^th grade M(SD)	t	df	Cohen’s d
Basic	3.53 (1.16)	3.30 (1.05)	498.76 (87.61)	489.26 (70.00)	−2.33*	391	0.12	503.84 (95.81)	498.96 (79.11)	−0.96	332	n.a.
Advanced	1.81 (0.95)	1.68 (0.95)	479.86 (79.04)	490.79 (65.45)	2.73**	391	0.14	515.96 (106.37)	509.62 (76.97)	−1.08	327	n.a.

*** p < .001, **p < .01, *p < .05.

Note: Girls’ and boys’ means are IRT estimates, which are rescaled and calculated so that average seventh-grade participant (participant in cohort 2, 2019) gains 500 points.

To compare what kinds of changes occurred before and during the COVID-19 pandemic, we utilized paired-samples t-tests separately for girls (n = 392–475) and boys (n = 328–431) and compared their answers from 2019 and 2020. This step also provided us with a more gendered picture of the phenomenon. In general, boys’ estimates regarding how often there were basic or advanced sociodigital practices at schools were higher than girls’ at both age cohorts and at both measure points. At primary school, there was a slight increase in students’ perceptions of the use of basic practices from 2019 to 2020, especially in girls’ perceptions (t(474) = 3.21, p < .001, d = .15). However, at middle school, the levels of perceived sociodigital practices were lower than at primary school, and middle school students perceived that sociodigital practices were used less often in the 8th grade than in the 7th grade, except for the 8th grade girls, who perceive there to be more often advanced practices than in the 7th grade (t(391) = 2.73, p < .01, d = .14).

However, all the changes and effect sizes were rather small. Boys in neither cohort perceived that there were barely any changes in the frequency of sociodigital practices at school (p > .05). This reveals that students’ perceptions of their digital reality are gendered. The results indicate that, in general, students’ perceptions regarding their schools’ sociodigital practices did not change much from 2019 to 2020, despite claims that a digital leap has occurred in schools.

Students’ perceptions of their sociodigital competences and mindsets from 2019 to 2020

To answer our second research question, we used a 15-item digital fluency measure and a 6-item sociodigital mindset measure. Table 3 below displays students’ self-assessments of their sociodigital competences and mindsets in both primary school (grades 5–6) and middle school (Grades 7–8). On average, academic sociodigital competences were mastered quite fluently (means: 3.82–4.26) in both cohorts and on both measure points. Middle school students’ self-assessed competences appeared to be more fluent than those of primary school students. Artistic sociodigital competences were mastered to some extent (means: 3.00–3.24), and technical competences were at the lowest fluency level (2.46–2.57). These indicate that those competences are mastered only slightly. Compared with academic competence, students’ self-assessed artistic and technical competences were almost at the same level in both cohorts. Sociodigital mindset means were slightly above the midpoint of the scale (3.29–3.56) and slightly higher at primary school than at middle school.

Table 3. Means of cohort totals and paired-samples t-tests of girls’ and boys’ sociodigital competences and mindsets.

	Total		Girls					Boys
	5^th grade M(SD)	6^th grade M(SD)	5^th grade M(SD)	6^th grade M(SD)	t	df	Cohen’s d	5^th grade M(SD)	6^th grade M(SD)	t	df	Cohen’s d
Academic	3.82 (0.76)	4.04 (0.64)	440.56 (93.69)	472.80 (79.26)	8.43***	475	0.39	503.54 (103.85)	512.50 (82.12)	2.18*	436	0.10
Artistic	3.24 (1.05)	3.07 (1.04)	500.90 (98.57)	492.84 (83.18)	−1.98*	475	0.09	524.60 (101.93)	503.98 (89.55)	−4.71***	434	0.23
Technical	2.57 (1.12)	2.49 (1.06)	468.48 (85.20)	471.20 (76.55)	0.76	474	n.a.	536.74 (92.43)	523.00 (85.89)	−3.28**	434	0.16
Wish for digital schoolwork	3.46 (1.15)	3.56 (1.09)	3.18 (1.14)	3.26 (1.06)	1.68	453	n.a.	3.78 (1.08)	3.92 (1.00)	2.61**	409	0.13
Digital self-efficacy	3.43 (1.03)	3.43 (1.05)	3.16 (0.98)	3.13 (0.97)	−0.67	449	n.a.	3.76 (1.00)	3.79 (1.00)	0.63	411	n.a.
	7^th grade M(SD)	8^th grade M(SD)	7^th grade M(SD)	8^th grade M(SD)	t	df	Cohen’s d	7^th grade M(SD)	8^th grade M(SD)	t	df	Cohen’s d
Academic	4.07 (0.63)	4.26 (0.51)	477.26 (86.05)	499.32 (71.71)	5.17***	393	0.26	531.60 (95.33)	529.02 (69.82)	−0.52	333	n.a.
Artistic	3.13 (0.99)	3.00 (0.90)	490.52 (88.72)	483.81 (71.26)	−1.68	393	n.a.	510.70 (101.76)	497.61 (82.34)	−2.46*	332	0.14
Technical	2.56 (1.07)	2.46 (0.97)	472.41 (83.39)	469.78 (72.09)	−0.73	393	n.a.	532.42 (91.38)	518.31 (80.26)	−2.92**	332	0.16
Wish for digital schoolwork	3.37 (1.17)	3.25 (1.10)	3.01 (1.11)	2.89 (1.03)	−2.37**	369	0.12	3.79 (1.09)	3.70 (1.00)	−1.42	305	n.a.
Digital self-efficacy	3.29 (1.00)	3.30 (1.01)	2.98 (0.94)	2.95 (0.97)	−0.32	365	n.a.	3.70 (0.89)	3.75 (0.88)	0.86	301	n.a.

*** p < .001, **p < .01, *p < .05.

Note. The mean values of girls’ and boys’ sociodigital competences are IRT estimates, which are rescaled and calculated so that the average 7^th-grade participant (participant in cohort 2, 2019) gains 500 points. Sociodigital mindset means are the mean values of measures (scale of 1–5).

We utilized paired-samples t-tests separately for girls’ (n = 366–476) and boys’ (n = 302–437) answers in 2019 and 2020 to examine what kinds of changes had occurred in students’ sociodigital competences and mindsets before and during the COVID-19 pandemic. The level of self-assessed sociodigital competence was gendered, as boys’ estimates of their sociodigital competence were thoroughly higher compared to girls’ estimates at both age cohorts and at both measurement points (Table 3). Furthermore, girls’ estimates were below the average of 500 points, whereas boys’ estimates were above the average. As for the changes between 2019 and 2020, both cohorts assessed their academic sociodigital competences to be more fluent in 2020 than in 2019, except for boys at middle school who perceived their competences to be quite stable (p > .05). On the contrary, artistic and technical competences were assessed to be mastered less fluently in 2020 compared with 2019 at both cohorts, and this decrease was especially evident in boys’ self-assessments (p < .05).

As for sociodigital mindset, primary school girls’ means were just above the midpoint of the scale but dropped below it at middle school, whereas boys’ estimates were evenly close to 4 (on a scale of 1–5). These indicate that the sociodigital mindset is gendered and in favor of boys. Furthermore, wish for digital schoolwork increased in primary school boys’ estimates from 2019 to 2020 (t(409) = 2.61, p < .01, d = .13) but decreased in middle school girls’ estimates from 2019 to 2020 (t(369) = -2.37, p < .001, d = .12). Digital self-efficacy remained at the same level.

Nonetheless, the changes were rather small, as the effect sizes indicated. However, while girls’ sociodigital competences increased, their sociodigital mindsets stayed the same or even decreased slightly. A similar trend was not observed among boys. Overall, only students’ perceptions of their academic sociodigital competences increased from 2019 to 2020 for both cohorts. At the same time, the fluency levels of their artistic and technical competences decreased, while their sociodigital mindsets were rather stable.

Students’ sociodigital profiles and changes in profiles from 2019 to 2020

To answer our third research question regarding students’ sociodigital profiles, we analyzed sociodigital competences and mindset measures. We used LPA to identify the possible number of groups on all grade levels. The profile selection involved three steps: (1) acceptable profile statistics (Appendix: Table A4), (2) the same number of profiles on each grade, and (3) a graphical solution (Figures 1A and 1B) to support differences other than simply the level differences among profiles. We used the Akaike information criterion (AIC) and Bayesian (BIC) information criterion to evaluate the profile solution goodness. Next, we evaluated model entropy, which was at a decent level on every profile (over .70). Because bootstrapped likelihood ratio test (BLRT) estimated profile solution significantly better than one class smaller group, we used the Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR) to compare the solutions. As the VLMR results showed that at least one profile was not more significant than three or four profiles, we evaluated the profile numbers graphically to decide on the proper number of profiles. We found that four profiles revealed crossing differences with sociodigital mindsets and competences for almost all profiles (except 7th grade). Therefore, we decided to use a four-profile solution for all grades, even though statistical numeracy did not reveal this to be an optimal solution for each grade separately.

We identified four sociodigital profiles as follows: Profile 1: Inexperienced (11%–23%) had the lowest self-evaluated sociodigital competences and sociodigital mindsets. Profile 2: Enthusiastic (40%–45%) had fairly low self-evaluated sociodigital competences but mediocre sociodigital mindsets (Table A5). Profile 3: Humble (7%–34%) had rather high self-evaluated sociodigital competences but low sociodigital mindsets, except for 7th graders. Profile 4: Driven (10%–30%) had the highest self-evaluated sociodigital competences and the highest sociodigital mindsets.

Figures 1A and 1B display primary and middle school students’ sociodigital profiles, respectively. The differences between sociodigital competences across profiles were narrower in 2020 than in 2019, but sociodigital mindsets appeared to remain at the same level. These results are also confirmed by the paired-samples t-tests in a previous section (i.e. Students’ Perceptions of Sociodigital Competence and Mindset from 2019 to 2020), in which only small or no changes were found in students’ sociodigital mindsets. This section also indicated that boys’ self-assessed sociodigital competences tended to decrease more than those of girls.

The Inexperienced, the profile with the lowest self-assessed sociodigital competences and mindsets, seemed to be a very gendered profile, as girls were overrepresented in both cohorts and in both measure points (Table 4).

Table 4. Distribution of girls and boys and the corresponding total percentages on the four profiles.

Grade (n)		Inexperienced	Enthusiastic	Humble	Driven
5^th (946)	Girls	31%	46%	9%	14%
	Boys	13%	43%	5%	39%
	Total	23%	44%	7%	26%
6^th (947)	Girls	22%	43%	16%	19%
	Boys	9%	37%	12%	42%
	Total	16%	40%	14%	30%
7^th (769)	Girls	16%	54%	26%	4%
	Boys	6%	35%	42%	17%
	Total	11%	45%	34%	10%
8^th (769)	Girls	28%	43%	18%	11%
	Boys	6%	44%	15%	35%
	Total	18%	43%	17%	22%

The Enthusiastic profile represents the largest profile on all grade levels, which means that there are many students who believe their sociodigital competences are not very high but are nonetheless rather eager users of digital technology. Enthusiastics in the 7th grade, however, were exceptions because their mindsets were below the mean; however, when they reached 8th grade, their mindset increased to above the mean. When examining the gender divide in students’ profiles, boys and girls were equally represented in the Enthusiastic profile, except in the 7th grade when girls were overrepresented. This probably explains the difference in the mindset levels in the Enthusiastic profile at the 7th grade compared with other grades, as revealed in an earlier section where girls’ sociodigital mindsets are lower than those of boys.

The Humble profile, which showed a high sociodigital competence but low sociodigital mindset, was the smallest profile except in the 7th grade. In the 5th grade, there were only a few students classified as Humble, but by 7th grade, one-third of the students belonged to this profile, and their sociodigital mindsets also increased above mean at this measure point. When examining the gender divide, girls and boys were represented equally in Humble, except for the 7th grade, where boys were overrepresented. Again, this probably explains why Humble’s mindset level in the 7th grade is higher than those in other grades, as revealed in a previous section where boys’ sociodigital mindsets were higher than those of girls.

The Driven, with the highest sociodigital competence as well as the highest sociodigital mindset, is another very gendered profile. Throughout all grade levels, boys were overrepresented in this profile, in line with our results in a previous section, where boys’ self-estimations of their sociodigital competences and mindsets were higher than girls’ estimations.

Discussion

This article aimed to (a) examine the changes in Finnish primary and middle school students’ perceived sociodigital study practices, sociodigital competences, and sociodigital mindsets and (b) trace their sociodigital profiles. Data were collected before (2019) and during (2020) the COVID-19 pandemic, which forced schools to shift to digitally mediated distance learning or hybrid instructional practices. The data analyzed consisted of 947 cohort 1 students (5th grade in 2019 and 6th grade in 2020) and 771 cohort 2 students (7th grade in 2019 and 8th grade in 2020) from different comprehensive schools in the city of Helsinki. As follows, we summarize the results regarding each of the research questions.

Regarding our first research question, ‘How did students’ perceptions of their schools’ sociodigital practices change from 2019 to 2020?’ we found that, in general, primary school students experienced an increase in their school’s sociodigital practices. This is evident specifically in girls’ perceptions of basic digital practices. On the contrary, middle school students experienced a decrease in their school’s digital practices, with the exception of 8th grade girls. In general, girls are mainly the ones who appeared to experience changes in their schools’ sociodigital practices. During conventional times, differentiation enabled students to select their preferred methods of working, which is likely to be digital among boys. However, during the COVID-19 distance learning period, everybody was expected to engage in digital practices of learning, and girls experienced this as an increase in digital study practices. Nevertheless, the changes were rather small, indicating that schools only took a minor ‘digital leap’ in terms of hybrid or distance education without deeper systemic change in the practices of digital learning and instruction. Although the COVID-19-induced shift in school practices is a remarkable phenomenon, digitalization of education remains a complex and long-standing process (Lund & Aagaard, 2017) that took its first steps during the pandemic. It is possible that these development steps will be notable only a few years after the teachers’ new skills have been implemented in teaching, and new practices have become more established in everyday schoolwork.

Second, we asked, ‘How did students’ sociodigital competences and sociodigital mindsets change from 2019 to 2020?’ Regarding this question, we found that students’ self-evaluated academic sociodigital competences increased from 2019 to 2020, while their artistic and technical competences decreased. The COVID-19 distance learning period probably provides an explanation for the increase in academic sociodigital competences, as academic skills are the ones used during distance learning. During the COVID-19 pandemic, schools were not likely to have the possibility of cultivating students’ creative competences in using sociodigital technologies called for by prior digital competence and fluency research (e.g. Barron, 2014; Blikstein, 2013; Ito et al., 2013, 2019; Kafai & Peppler, 2011; Peppler et al., 2022).

Furthermore, regarding the sociodigital mindset, primary school boys’ digital engagement (wish for digital schoolwork) increased, which means they would have liked to use digital tools and practices, including advanced levels of technology use, even more often at school. At the same time, middle school girls’ digital engagement decreased from 2019 to 2020, possibly implying that they had a more instrumental relation to digital technology and did not want to use digital technology more than schoolwork required. However, there were no changes in the other aspect of the sociodigital mindset, namely, digital self-efficacy.

Third, we investigated the answer to the question, ‘What kinds of sociodigital profiles can be found in Finnish students?’ We identified four profiles: Inexperienced (the lowest self-evaluated sociodigital competence and sociodigital mindset), Enthusiastic (fairly low self-evaluated sociodigital competence, but mediocre sociodigital mindset), Humble (rather high self-evaluated sociodigital competence, yet low sociodigital mindset), and Driven (highest self-evaluated sociodigital competence and sociodigital mindset). Notable are gender differences in students’ profiles; that is, girls and boys are overrepresented in the Inexperienced and Driven profiles, respectively. However, we did find that the Enthusiastic profile, which represented both boys and girls fairly equally, was the largest profile group on all grade levels. This indicates that despite the imbalance between the Inexperienced and Driven profiles, about 40% of the students are still eager to learn and use digital technologies in schools, even if they may not believe their sociodigital competences to be high. We also found that the between-profile differences in sociodigital competence narrowed in 2020 compared with that in 2019, but each profile’s sociodigital mindset appeared to remain at approximately the same level.

Reflections of the research results

We conclude that this study attests to the importance of collecting and reporting data about students’ digital fluency (Barron et al., 2009, 2010) by examining interrelations between sociodigital practices, competences, mindsets, and profiles. The novel sociodigital profiles constructed in this study, which combine the concepts of digital mindset and digital competence with the perceptions of sociodigital practices, offer the possibility of viewing students’ digital fluency from a multilevel perspective. The sociodigital profiles constructed in this study highlight the role of the sociodigital mindset beyond mere digital competences. Digital efficacy (Kim & Glassman, 2013; Tømte & Hatlevik, 2011; Tsai et al., 2011) and engagement (Hietajärvi et al., 2020; Ito et al., 2019; Jenkins & Ito, 2015; Peppler et al., 2022) appear to significantly shape students’ trajectories of cultivating their digital fluency in and out of schools.

The study also highlights importance of providing students structured opportunities for developing their sociodigital competences. Building sociodigital competences is critical for overcoming digital divides (OECD, 2012; Van Dijk, 2020) and creative participations gaps (Jenkins & Ito, 2015). In accordance with Barron et al. (2014), Blikstein (2013), Kafai and Peppler (2011), Peppler et al. (2022) and Jenkins and Ito (2015), we would like to emphasize importance of acknowledging all dimensions, including the artistic and technical, beyond merely the academic sociodigital competence. Previous quantitative studies may have failed to adequately measure the full diversity of the creative and artistic forms of digital technologies. This is partially because tools and practices of digital fabrication and makes-centered learning have only relatively recently started to shape practices of using digital technologies at schools (Blikstein, 2013; Kafai & Peppler, 2011; Keune & Peppler 2019; 2022; Korhonen et al., 2022a; Rouse & Gillespie Rouse, 2020).

Our results also imply that competence and mindset do not have as straightforward a relationship as traditionally assumed and that a positive sociodigital mindset would function as a strengthening mediator for sociodigital competence. The COVID-19 shock forced schools to significantly transform their pedagogic and organizational practices (Iivari et al., 2020; Okkonen, 2020). Yet, remote teaching was implemented various ways in different municipalities (Korte et el., 2022), and teachers and students had mixed negative and positive experiences (Loukomies & Juuti, 2021; Niemi & Kousa, 2020). Overall, schools did not appear to go through as significant digital leap as earlier surveys (e.g. Karvi 2020) suggest. This result, combined with new knowledge about students’ sociodigital competences, mindsets, and profiles, should be considered at all educational levels when planning and organizing the next supporting steps for developing Finnish teachers’ and students’ digital fluency.

Limitations and future research

Like any other investigation, the present study also has limitations. First, the participants were asked to respond to the SDPi, which involved a number of emergent measures regarding sociodigital competence, sociodigital mindset, and sociodigital study practices. Yet, the measures were elaborated on by relying on numerous earlier studies among Finnish students, such as Hietajärvi et al. (2016, 2020), arguably measuring adequately the targeted phenomena. The measures were developed to correspond to current practices of digitally mediated learning and teaching. Furthermore, the items included examples of various applications and software that we thought the students were familiar with.

Second, although the present sample sizes were relatively large, the measures were based on students’ self-reports with well-known limitations, such as social desirability bias (Nederhof, 1985). For instance, social desirability may have led to the inflation of the sociodigital competences of boys. Further, the sociodigital practices of schools were assessed by using intensity of perceived activity scales rather than, for instance, teachers’ reports or actual learning analytic data to confirm students’ self-reports. It is likely that the students highlighted practices they are themselves familiar with or inspired by.

This study reports the first two years (2019 and 2020) of SDPi data collection developed in our research group. Further studies are required to examine how students’ sociodigital practices, competences, mindsets, and profiles changed over time during the years following COVID-19. There is also a need to continue studies on how students’ sociodigital competence development and digital engagement can be supported in the educational context (i.e. research about digital-pedagogical methods that support both creative and engaging digital competence and the mindset development of students). Earlier studies on digital-pedagogical methods have indicated the benefits of integrating STEAM subjects with arts and craft studies to enable sustained learning-by-making projects that provide ample opportunities to carry out creative knowledge-creating inquiries and learn sociodigital competence (e.g. Blikstein, 2013; Hakkarainen et al., 2015; Korhonen et al., 2022b; Peppler et al., 2022). In light of the results of the present study, it is important to continue these digipedagogical studies by utilizing new knowledge about students’ sociodigital competence and mindset dimensions and, especially, students’ profiles.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was carried out with funding from the Strategic Research Council (Grant Nos. 312527 and 336064 [Growing Mind]).

Notes on contributors

Tiina Korhonen

Tiina Korhonen, PhD, is the University Lecturer (Learning Innovations in Digital Society) and head of Innokas Network (www.innokas.fi/en), coordinating nationwide Innovation Education activities for over 750 schools in Finland and leading the Learning and Teaching in Digital Environments post-graduate specialization studies program in University of Helsinki. Dr. Korhonen’s professional interests lie in the wide landscape of 21st century learning and development of innovative educational practice in the context of the digital society, with special focus on the practical opportunities available through digital tools and processes, including digital learning environments, AI, VR, computational thinking, and robotics.

Noora Laakso

Noora Laakso, MEd, is a PhD researcher in the Department of Education, University of Helsinki. Laakso’s working experience within research projects has an emphasis on data collection and conducting a variety of statistical analyses. Her doctoral research is about comprehensive school students’ digital game-making projects focusing on 21st century learning, and the interconnection of informal and formal learning applying a mixed-methods approach.

Aino Seitamaa

Aino Seitamaa, MEd, is a Learning Design Specialist in the EdTech field, specializing in course creation in online learning platforms. Her master’s thesis on sociodigital mindset paved way to further research learning analytics and virtual reality in competence development.

Visajaani Salonen

Reito Visajaani Salonen is Project planner in the Institute for Social Sciences and Humanities (HSSH), University of Helsinki. Salonen has, for 7 years, carried out methodological support and develompent on statistical analysis in quantitative and physiological data, such EDA and eye-tracking data, in the University of Helsinki. Salonen is also PhD student in the Faculty of Educational Sciences (UH). Salonen is actively collaborating with many projects and designing facilities for easier data collection and analysis internationally.

Netta Tiippana

Netta Tiippana, MEd, is a PhD researcher in the Department of Education, University of Helsinki. She has gained working experience in the areas of international education, digital learning, professional development and training management. She has managed development of online workshops and novel eLearning products, as well as capacity-building projects in the field of education and community management in various international contexts. Her professional interests lie in organizational development and networked learning.

Jari Lavonen

Jari Lavonen, is a Professor of Physics and Chemistry Education at the University of Helsinki, Finland. He has been researching science and teacher education for the last 35 years. His special focuses are: project-based learning, student interest and engagement, career awareness, transversal competences and teacher education. He worked recently as a Director of the National Teacher Education Forum and is working as a Chair of the Finnish Matriculation Examination Board.

Kai Hakkarainen

Kai Hakkarainen, is Professor of education in the Department of Education, University of Helsinki. With his colleagues, Hakkarainen has, for 20 years, carried out learning research based on psychology and cognitive science at all levels, from elementary to higher education. During recent years, Hakkarainen’s research activity has expanded toward investigating personal and collective learning processes taking place in communities and networks of experts, including knowledge-intensive professional organizations and academic research communities.

Appendix

Table A1A. PCA with oblimin rotation of sociodigital practices measure, cohort 1 in 2019: 5th grade.

	Advanced digital practices (PC1)	Basic digital practices (PC2)
% of variance explained by the component	39.29	10.49
I design or make digital games related to schoolwork. (Item 12)	0.773
In class, we build devices utilizing automation, such as robots or smart devices. (Item 10)	0.765
I make 3D drawings or 3D models. (e.g. Modelshop, Sketchup, and AutoDesk) related to schoolwork. (Item 9)	0.751
I code with some graphic or text-based programming language (e.g. Scratch, Tynker, Html, JavaScript, and Python) related to schoolwork. (Item 11)	0.738
I use virtual or augmented reality (VR/AR) related to schoolwork. (Item 7)	0.728
I maintain a blog or similar information platform related to the topic we are studying. (Item 5)	0.504	0.317
In class, we practice basic use of digital technology (e.g. sharing a document, word processing, and using e-mail and the Internet). (Item 1)		0.785
Teachers encourage me to share with others my know-how on devices, programs, and applications. (Item 4)		0.767
I make digital portfolios related to schoolwork (a portfolio is a digital folder, into which you collect samples of your own learning and skills). (Item 3)		0.687
During class, I am guided to recognize risks related to my own safety online and to avoid them. (Item 13)		0.685
I make multimedia presentations related to schoolwork. (Item 2)		0.599
I play learning games or other digital games related to schoolwork. (Item 8)		0.434
Teachers use animations or simulations in their teaching (simulation means devices or environments that mimic reality). (Item 6)	0.326	0.348
Mean (SD)	x.xx (sd.d)	c.cc (s.ss)
Number of items	7	6
Cronbach’s alpha	0.83	0.77
N	1204	1206

Table A1B. PCA with oblimin rotation of sociodigital practices measure, cohort 2 in 2019: 7th grade.

	Advanced digital practices (PC1)	Basic digital practices (PC2)
% of variance explained by the component	39.01	11.49
I use virtual or augmented reality (VR/AR) related to schoolwork. (Item 7)	0.821
In class, we build devices utilizing automation, such as robots or smart devices. (Item 10)	0.821
I code with some graphic or text-based programming language (e.g. Scratch, Tynker, Html, JavaScript, and Python) related to schoolwork. (Item 11)	0.794
I make 3D drawings or 3D models (e.g. Modelshop, Sketchup, and AutoDesk) related to schoolwork. (Item 9)	0.787
I design or make digital games related to schoolwork. (Item 12)	0.771
I maintain a blog or a similar information platform related to the topic we are studying. (Item 5)	0.555
Teachers use animations or simulations in their teaching (simulation means devices or environments that mimic reality). (Item 6)	0.397
In class, we practice basic use of digital technology (e.g. sharing a document, word processing, using e-mail and the Internet). (Item 1)		0.828
I make multimedia presentations related to schoolwork. (Item 2)		0.713
Teachers encourage me to share with others my know-how on devices, programs, and applications. (Item 4)		0.707
During class I am guided to recognize risks related to my own safety online and to avoid them. (Item 13)		0.588
I make digital portfolios related to schoolwork (a portfolio is a digital folder, into which you collect samples of your own learning and skills). (Item 3)		0.587
I play learning games or other digital games related to schoolwork. (Item 8)		0.335
Mean (SD)	x.xx	x.xx
Number of items	7	6
Cronbach’s alpha	0.84	0.74
N	1157	1169

Table A2A. PCA with oblimin rotation of sociodigital competence, cohort 1 in 2019: 5th grade.

	Technical competence (PC1)	Academic competence (PC2)	Artistic competence (PC3)
% of variance explained by the component	43.328	8.022	6.735
I can build automated devices, such as robots or smart products (e.g. Lego EV3, Micro:bit, and Arduino). (Item 13)	0.692
I can code by using some block- or text-based programming languages (e.g. Scratch, Tynker, Html, JavaScript, Python, and EV3). (Item 14)	0.613
I can use spreadsheet programs, such as Excel. (Item 4)	0.471	0.365
I can use word processing programs, such as Word. (Item 3)		0.828
I can use cloud storage services (e.g. OneDrive, Office365, Google Drive, and iCloud). (Item 1)		0.726
I can make multimedia presentations, which include at least image and text (e.g. PowerPoint, Google Slides, and Prezi). (Item 5)		0.646
I can solve basic IT problems, like reinstall a program and check the Internet connection. (Item 2)		0.597
I can recognize risks related to my own safety online and avoid them. (Item 15)	−0.443	0.533	0.337
I can edit videos (e.g. iMovie, VSDC, and PowToon). (Item 8)			0.823
I can edit images online or with image processing programs (e.g. Gimp, Pinta, and Fotor). (Item 6)			0.804
I can draw or paint with painting and graphic programs (e.g. Paint, Krita, and Inkscape). (Item 7)			0.767
I can create and maintain a blog or a website (e.g. WordPress, Blogger, WiX, and Weebly). (Item 12)	0.309		0.593
I can make animations (e.g. StopMotion, Scratch Jr, Blender, Pencil2D, and Animaker). (Item 9)			0.536
I can make digital 3D drawings or models (e.g. Blender, FreeCAD, and TinkerCAD). (Item 10)	0.464		0.528
I can make music using digital technologies (e.g. GarageBand, Audacity, and Ardour). (Item 11)	0.370		0.421
Mean (SD)	x.x	x.x	x.x
Number of items	4	6	5
Cronbach’s alpha	0.808	0.772	0.821
N	1217	1224	1213

Table A2B. PCA with oblimin rotation of sociodigital competence, cohort 2 in 2019: 7th grade.

	Technical competence (PC1)	Academic competence (PC2)	Artistic competence (PC3)
% of variance explained by the component	42.914	10.058	6.464
I can build automated devices, such as robots or smart products (e.g. Lego EV3, Micro:bit, and Arduino). (Item 13)	0.862
I can code by using some block- or text-based programming languages (e.g. Scratch, Tynker, Html, JavaScript, Python, and EV3). (Item 14)	0.816
I can make digital 3D drawings or models (e.g. Blender, FreeCAD, and TinkerCAD). (Item 10)	0.642
I can make music using digital technologies (e.g. GarageBand, Audacity, and Ardour). (Item 11)	0.523
I can use spreadsheet programs, such as Excel. (Item 4)	0.477	0.357
I can use word processing programs, such as Word. (Item 3)		0.827
I can use cloud storage services (e.g. OneDrive, Office365, Google Drive, and iCloud). (Item 1)		0.758
I can make multimedia presentations, which include at least image and text (e.g. PowerPoint, Google Slides, and Prezi). (Item 5)		0.720
I can solve basic IT problems, like reinstall a program and check the Internet connection. (Item 2)		0.635
I can recognize risks related to my own safety online and avoid them. (Item 15)		0.603
I can edit videos (e.g. iMovie, VSDC, and PowToon) (Item 8)			0.802
I can draw or paint with painting and graphic programs (e.g. Paint, Krita, and Inkscape). (Item 7)			0.766
I can edit images online or with image processing programs (e.g. Gimp, Pinta, and Fotor). (Item 6)			0.742
I can make animations (e.g. StopMotion, Scratch Jr, Blender, Pencil2D, and Animaker). (Item 9)			0.631
I can create and maintain a blog or a website. (Item 12)	0.391		0.447
Mean (SD)	x.x	x.x	x.x
Number of items	4	6	5
Cronbach’s alpha	0.817	0.798	0.822
N	1185	1183	1187

Table A3A. PCA with oblimin rotation of sociodigital mindset, cohort 1 in 2019: 5th grade.

	Wish for digital schoolwork (PC1)	Digital self-efficacy (PC2)
% of variance explained by the component	63.20	14.24
I am more enthusiastic about my schoolwork when I am allowed to use digital technology. (Item 2)	0.925
I study school things more diligently when I am allowed to use digital technology. (Item 3)	0.922
I would like to use digital technology more in my schoolwork. (Item 1)	0.847
I trust my ability to learn things related to digital technology. (Item 8)		0.895
I like to solve challenging problems related to digital technology. (Item 7)		0.878
I am prepared to put in a lot of effort to learn something related to digital technology. (Item 6)		0.771
Mean (SD)	x.xx (sd.d)	c.cc (s.ss)
Number of items	3	3
Cronbach’s alpha	0.83	0.77
N	1204	1206

Table A3B. PCA with oblimin rotation of sociodigital mindset, cohort 2 in 2019: 7th grade.

	Wish for digital schoolwork (PC1)	Digital self-efficacy (PC2)
% of variance explained by the component	64.44	16.26
I am more enthusiastic about my schoolwork when I am allowed to use digital technology. (Item 2)	0.948
I study school things more diligently when I am allowed to use digital technology. (Item 3)	0.931
I would like to use digital technology more in my schoolwork. (Item 1)	0.894
I am prepared to put in a lot of effort to learn something related to digital technology. (Item 6)		0.897
I like to solve challenging problems related to digital technology. (Item 7)		0.868
I trust my ability to learn things related to digital technology. (Item 8)		0.842
Mean (SD)	x.xx (sd.d)	c.cc (s.ss)
Number of items	3	3
Cronbach’s alpha	0.91	0.84
N	1154	1140

Table A4. LPA statistical model fit descriptives for possible solutions in profile analysis.

	5th grade	6th grade	7th grade	8th grade
Loglikelihood
3 Profiles	−7863.974	−7356.218	−7504.810	−6958.747
4 Profiles	−7763.470	−7230.701	−7414.868	−6865.571
5 Profiles	−7672.185	−7140.408	−7323.218	−6785.875
AIC
3 Profiles	15771.949	14756.437	15053.620	13961.495
4 Profiles	15582.941	14517.402	14885.736	13787.142
5 Profiles	15412.370	14348.815	14714.436	13639.749
BIC
3 Profiles	15885.021	14869.122	15165.893	14073.641
4 Profiles	15726.851	14660.820	15028.629	13929.873
5 Profiles	15587.119	14522.965	14887.949	13813.066
Entropy
3 Profiles	0.780	0.757	0.754	0.749
4 Profiles	0.777	0.744	0.759	0.711
5 Profiles	0.753	0.735	0.728	0.711
p BLRT
3 Profiles	0.0000	0.0000	0.0000	0.0000
4 Profiles	0.0000	0.0000	0.0000	0.0000
5 Profiles	0.0000	0.0000	0.0000	0.0000
p VLMR
3 Profiles	0.0000	0.1649	0.0126	0.0000
4 Profiles	0.0623	0.0051	0.0407	0.0000
5 Profiles	0.0139	0.0494	0.0057	0.0878
Class sizes (%)
3Profiles	24/27/49	18/31/51	17/34/49	18/25/56
4 Profiles	9/23/25/43	16/16/30/38	10/12/34/44	18/20/22/40
5 Profiles	10/11/19/28/32	12/13/18/27/30	10/15/20/23/31	10/15/20/23/32

Table A5. Descriptive statistics for students’ sociodigital profiles.

	Inexperienced		Enthusiastic		Humble		Driven
	Lowest skills, lowest mindset		Low skills, mediocre mindset		High skills, low mindset		Highest skills, strongest mindset
Grade (n)	M skills (SD)	M mindset (SD)	M skills (SD)	M mindset (SD)	M skills (SD)	M mindset (SD)	M skills (SD)	M mindset (SD)
5 (947)	349.93 (64.58)	2.56 (0.73)	480.64 (47.48)	3.66 (0.63)	529.57 (55.51)	1.97 (0.53)	632.86 (59.49)	4.31 (0.61)
6 (947)	383.70 (74.36)	1.96 (0.57)	444.03 (49.89)	3.63 (0.51)	556.15 (58.96)	2.84 (0.49)	590.02 (69.30)	4.47 (0.45)
7 (771)	351.96 (59.04)	1.96 (0.79)	455.13 (43.61)	3.11 (0.72)	560.77 (38.83)	3.80 (0.77)	698.57 (53.38)	4.32 (0.76)
8 (771)	419.44 (58.71)	1.86 (0.53)	455.48 (42.86)	3.47 (0.55)	555.47 (50.16)	2.83 (0.48)	597.00 (62.31)	4.36 (0.52)