Open access peer-reviewed chapter
Distribution of the sample.
Abstract
The advancement and widespread use of the Internet in higher education have created the need to develop and monitor the digital skills of students at this level. Consequently, the objective of the research was to validate a digital capital scale applicable to the educational field and verify its validity among Spanish-speaking higher education students. For this purpose, a sample of students from Chile and Costa Rica was included, to whom a scale was administered, and confirmatory factor analysis and invariance analysis were subsequently conducted. The results showed that the resulting factors exhibit good fit indices. Additionally, from a theoretical perspective, the behaviors included in the construct correspond to the specific demands of digital societies and higher education. Therefore, it is reasonable to assume a specific capital that meets these requirements. In conclusion, it can be assumed that there exists a valid instrument for measuring digital capital based on three behaviors: information-seeking, information production, and information exchange. However, it is necessary to improve its composition to achieve a scale that allows for reliable comparisons among students from different countries.
Keywords
- digital capital
- higher education
- digital skills
- scale validation
- digitization of education
1. Introduction
The Internet has become one of the most important cultural artifacts in contemporary societies, forming the material basis of what has been called the information society [1, 2] or digital society [3, 4]. The importance that it has acquired derives from its ability to serve as a catalyst for new social dynamics, creating opportunities in different fields and becoming a key part of our daily lives.
In this digital context, technological expertise can be used as an exchange value, providing opportunities to those with greater competencies. However, it can also generate disadvantages for those who lack these competencies. As such, researchers have begun to observe how low levels of digital engagement can negatively impact the development of new forms of capital, replicating and potentially amplifying social inequities in other areas, including the educational field.
From the sociology of education, the concept of cultural capital, coined by Bourdieu and Passeron [5], has been identified as one of the factors explaining educational inequalities. According to Bourdieu and Passeron [6], students from more privileged social classes possess greater reserves of cultural capital, which is transmitted intergenerationally within families and, in turn, leads to favorable academic outcomes.
Although Bourdieu does not provide a precise definition of cultural capital, it can be understood as familiarity with high culture and the ability to convert this familiarity into other types of capital [7]. In this regard, Bourdieu identified three types of cultural capital: embodied, referring to cultural tastes and behaviors; institutionalized, referring to academic credentials; and objectified, referring to the possession of cultural objects [8]. These forms of capital are transmitted and activated by parents and put into practice in the educational field by students, generating direct benefits for their academic performance. For example, it has been shown that students whose parents have higher education are more likely to perform well academically and complete a university degree compared to first-generation students [9].
However, in today’s societies, access to culture and knowledge is no longer limited to the educational system or family transmission. The reach of information and communication technologies, particularly the Internet, has driven various transformations in our daily lives [3], which is especially evident among younger generations, who frequently use the Internet for a wide range of purposes [10], creating opportunities for autonomous learning and thereby acquiring cultural capital [11, 12]. Indeed, some studies have shown that young people use the Internet to complement school content or to engage in informal learning in various areas of interest [13, 14].
Additionally, considering current social and technological changes, the nature of the concept of cultural capital proposed by Bourdieu may no longer fully apply [15], and the traditional understanding of culture may be complemented or even displaced by new cultural codes and practices driven by younger generations and widely disseminated through the Internet [16, 17, 18]. This shift moves toward a more heterogeneous concept of culture, rejecting hegemonic notions of one culture being superior to or more sophisticated than others.
At the same time, the skills needed to effectively use digital technologies have become a constitutive part of the educational experience, particularly among higher education students, who increasingly rely on the Internet to perform and achieve favorable academic outcomes [19, 20]. In this context, some studies have shown that appropriate and focused use of digital technologies can help students achieve better academic performance [19, 20, 21].
In light of the growing reach of information and communication technologies, some researchers have proposed updating the concept of cultural capital to include a digital dimension [22, 23, 24, 25]. However, most of these initiatives have been largely rhetorical, and existing empirical studies have focused primarily on the general population [26, 27, 28, 29, 30], without considering the specific skills or uses required in the educational field.
Consequently, the objective of this research was to validate a digital capital scale applicable to the educational field, particularly in higher education, where the use of digital technology for academic tasks is the norm and can make a significant difference in students’ academic performance. Additionally, a second objective was to test its applicability and validity among Spanish-speaking higher education students from different Latin American countries. For this purpose, the study involved students from a South American country (Chile) and students from a Central American country (Costa Rica).
2. Theoretical framework
Fields refer to a specific area of a social space with relative functional autonomy. They comprise a network of objective relationships between social positions. These positions are defined by the distribution of capital, and having such capital organizes access to specific advantages that are at play within the fields [31, 32].
Bourdieu [8] defines capital as accumulated work, whether material or incorporated, that can be mobilized to produce value. These assets derive from the development and maintenance of material resources, knowledge and social relationships [24]. Following Bourdieu [8], there are three fundamental forms of capital: (i) economic capital, which comprised monetary and financial resources; (ii) social capital, which comprised resources that can be mobilized by actors as a function of their belonging to social networks and organizations; and (iii) cultural capital, which comprised cultural conditions and knowledge.
Subjects’ social position depends on the possession of these types of capital and their volume and structure. As such, for example, “two individuals endowed with an equivalent overall capital can differ in their social position as well as in their stances, in that one holds a lot of economic capital and little cultural capital while the other has little economic capital and large cultural assets” ([32], p. 72).
Agents move between different fields and need codes, language and capital to perform in each of them, as they qualify them and give them legitimacy. In this way, digital capital would be one of the assets necessary to successfully perform in various social fields. In the educational field, the capacity to use digital technologies to access information and solve problems is associated with differences in learning achievements that are independent of other forms of capital [33, 34]. Some authors have reported that university students’ digital competencies present a positive association with their informal learning via the Internet [34, 35].
Accordingly, digital capital could support the further acquisition of skills and academic knowledge that is not typical of students with narrow experiences of technology. However, the digital capital is unevenly distributed, reason for which it acquires the category of capital [32]. Consequently, it is proposed to conceptualize digital competences as a form of digital capital, given that—unlike other competences—they are unevenly distributed across the population. Digital capital interacts with other forms of capital, such as economic, cultural, social and personal [27, 29]. Moreover, the possession of this capital can facilitate the development of other forms of capital, such as educational or cultural capital [11, 12, 13, 14].
2.1 The theorical development of digital capital
Recognizing that capital can take on various forms helps to explain the structure and dynamic of differentiated societies [32]. In this context, some authors have proposed the concept of digital capital [23, 25, 36, 37], but there is as yet no consensus on its definition, and it is possible to identify different approaches to conceptualization and measurement.
The first considers digital access, use and abilities as a new aspect of cultural capital. Some authors [16, 22] have argued that attitudes toward computers, the use of technologies and digital competencies could be seen as contemporary indicators of cultural capital.
Selwyn [38] proposed understanding digital capital as a subgroup of economic, cultural and social forms of capital that together configure individuals’ commitment to ICTs. Economic capital is represented by access to technologies and cultural capital by the time invested in improving digital skills, certified training in ICTs and the consumption of digital cultural products. For its part, social capital is symbolized by the network of contacts on digital platforms. Along this line of inquiry, researchers in Peru [39] determined that digital capital can be represented based on three components: (i) social digital capital, which refers to the accumulation of relationships through digital media; (ii) cultural digital capital, which refers to the accumulation of objects, preferences and interests acquired through digital practices; and (iii) productive digital capital, which is the accumulation of skills that allow for the effective use of ICTs and procurement of results.
Currently, the proposal is to conceptualize digital capital as a secondary form of capital that is different from the primary ones, such as economic, social and cultural capital [23, 25, 26, 37, 40]. In this context, Ignatow and Robinson [23] define digital capital as the scope, scale and sophistication of online behaviors, while Park [37] defines it as a digital technological ecosystem of individuals that guides how users are involved with digital technologies. Ragnedda [26] offers a similar definition, proposing to conceptualize digital capital as the accumulation of digital competencies and technology that influences the quality of the Internet use experience.
Regarding the measurement of Digital Capital, McConnell and Straubhaar [36] proposed an index that groups the confidence level of survey respondents regarding the completion of a set of seven activities on a computer. Similarly, Lee and Chen [41] conceptualized techno-capital using two indicators: one reflecting digital competencies and the other focused on digital cultural production.
Calderon [42] focused on two dimensions: (i) incorporated technological capital, which refers to a set of digital skills, and (ii) objectivized technological capital, which is access to mobile phones, computers, video games and other items. The operational definition is similar to that proposed by Ragnedda, Ruiu and Addeo [26, 29], whose digital capital index is associated with socioeconomic and sociodemographic patterns, such as age, income, educational level and place of residence, but not with gender.
In a more recent proposal regarding digital capital, researchers in Finland measured digital capital as the level of skills in four areas: operational skills, information navigation skills, creative and mobile skills [28]. For their part, those who have applied the concept to teachers have used the DigCompEdu European framework to develop the digital skills items [43].
However, this proposal remains general in nature and may not be fully relevant or applicable to the context of higher education. Furthermore, as these initiatives originate in Europe, the linguistic and conceptual frameworks they employ do not always align with the sociocultural and educational realities of Latin America.
2.2 An operational proposal of digital capital
Based on the information reviewed, we chose to consider digital capital as a secondary form of capital [32] related to the accumulation of digital competencies [25, 26, 29]. Competencies are understood as a set of forms of knowledge, abilities, attitudes and values [44]. Given this definition, and considering that it is best to observe the deployment of competencies in order to measure them, we opted to consider behaviors that are important within various social fields, particularly the academic one in which students are immersed.
In this regard, the international frameworks that define digital competencies [45, 46] coincide around five areas: (i) informational literacy (accessing, localizing and evaluating information and data), (ii) communication and collaboration, (iii) digital content creation, (iv) security and (v) problem-solving. For its part, in the academic context, the International Computer and Information Literacy Study (ICILS) groups digital competencies into four conceptual categories: (i) understanding computer use, (ii) gathering information, (iii) producing information and (iv) digital communication [47]. Given that this research is focused on students and prioritizes Internet activities, the ICILS framework was adapted considering three groups of competencies: (i) searching out and selecting information, (ii) producing information and (iii) collaborating on and exchanging information.
The first set of proposed behaviors measures competencies related to searching for and selecting information. In this regard, international frameworks propose focusing on the capacity to seek out, evaluate and manage information [45, 46, 48]. Furthermore, some studies have included specific information search activities [49] or perceptions about the difficulty of finding information online [50]. However, the latter proposals were discarded because they depend on the type of data sought.
The second set of behaviors refers to competencies related to producing information. The international frameworks propose focusing on the capacity to develop digital content, integrate and reproduce digital content, program and understand licensing and copyright use [45, 46, 48]. Other research has focused on activities that reveal creative Internet use [50, 51].
The third set of behaviors refers to students’ competencies in regard to collaborating on and exchanging information. International frameworks include at least the capacity to interact, share and collaborate using digital technologies [45, 46, 48].
One of the key challenges in scale development is establishing cross-cultural validity, as cultural differences, language variations and contextual practices can influence the interpretation of items. Nevertheless, in the domain of digital competence measurement, some successful international comparative assessments have been documented. For instance, the ICILS study [47] is administered in 12 countries across various continents, including 2 from Latin America (Chile and Uruguay). In the European context, several researchers have administered scales measuring digital use and competence in both Spanish and Italian universities, which has enabled the generation of comparative findings [52, 53].
3. Methodology
A quantitative methodology was used to carry out the research, based on the design and validation of a scale. In order to evaluate the consistency and replicability of the items among Spanish-speaking students of different nationalities, a digital capital scale was administered to higher education students in Chile and Costa Rica, which involved the adaptation and updating of a previously validated instrument that had been applied to Chilean higher education students.
These two countries were selected because they share certain contextual and socio-educational characteristics that allow for comparability of results. Both Chile and Costa Rica are members of the Organization for Economic Co-operation and Development (OECD), the World Economic Forum, the G20, UNESCO and the United Nations, among others. This membership implies the adoption of common international objectives and goals, as well as their incorporation into national agendas and development plans.
In terms of educational informatics, the Global Competitiveness Report indicated that Costa Rica is the country that has most significantly integrated the development of digital skills into the educational field, followed by Chile [54]. Both countries are considered pioneers in Latin America in implementing educational policies for integrating technologies into classrooms [55, 56].
3.1 Participants
For the application and validation process of the digital capital scale, a random selection of higher education students was conducted from a public university in southern Chile and a public university in Costa Rica. Specifically, 600 Chilean students and 600 Costa Rican students were selected, corresponding to a 95% confidence level and a 4% margin of error, based on the undergraduate student population size of each institution (11,546 and 11,420, respectively).
Additionally, for each initially selected student, an alternate replacement case was also chosen. Once the students were selected, an invitation to participate in the study was sent to their institutional email addresses. This invitation included information about the study and a link to access the informed consent form (Act number 033_18). Upon agreeing to participate, students were redirected to the survey.
As a result, the final sample consisted of 753 students, of whom 395 were from Chile and 358 from Costa Rica. This sample size ensures a 95% confidence level and a 5% margin of error for each stratum (Chile and Costa Rica). Table 1 presents the distribution of the sample by gender and stage of study (first stage, corresponding to the first and second year; and second stage, corresponding to the third and fourth year).
| Chile | Costa Rica | |
|---|---|---|
| Women | 229 | 195 |
| Men | 166 | 163 |
| First stage of study | 203 | 197 |
| Second stage of study | 192 | 161 |
Table 1.
3.2 Instrument
The scale used to measure digital capital corresponds to an updated version of the digital capital scale, which was previously applied and validated with Chilean university students [57]. In particular, the three original factors composed of 12 items were considered. However, given that one of the factors (collaboration and exchange of information) consisted of only three items, it was decided to strengthen this factor using items related to collaboration in digital environments proposed by van Laar et al. [58].
Once the scale was updated, cognitive interviews were applied, which allow determining problems with the writing of questions, order and format, in order to obtain suggestions for improvements from the study subjects themselves [59]. The interviews were conducted with seven higher education students in different programs who were distributed homogeneously by sex. Each student was asked to fill out the questionnaire and was then asked about their understanding of the questions, the appropriateness of the answers, the language used and the logic of their order. Furthermore, they were asked about the relevance of the items and their applicability in students’ daily activities.
The results of the interviews indicated that there were items related to collaboration that were redundant, and students did not perceive significant differences between them, which led to a reduction in the number of items for this dimension.
Finally, the scale for measuring digital capital was composed of 18 items. A Likert format with five response options was used, indicating the frequency with which the students engaged in the behaviors associated with the competencies in question (from
3.3 Data analysis
Following the review and cleaning of the databases, descriptive analyses were conducted to identify the characteristics of the sample. First, and as some authors suggest, the sample was randomly divided into two groups [60]. In a randomly selected subsample (150 students from Chile and 150 students from Costa Rica), after testing the metric characteristics of the items on the digital capital (kmo: 0.861; Bartlett: 0.00), an exploratory factor analysis was applied with SPPS version 25 software using Principal Axis Factoring and Oblimin rotation. The factor analysis produced a solution with three factors that together explain 41% of variance. Within each factor, only items loading over 0.4 were maintained. Furthermore, ambiguous items that loaded to more than one factor were eliminated. A total of three items were eliminated at this stage, leaving 15. Cronbach’s alpha statistic was applied to determine the reliability of the factors obtained.
Subsequently, confirmatory factor analyses were performed separately for the Chilean and Costa Rican samples using the Maximum Likelihood (ML) estimation method in R software (version 3.6.1), in order to assess the factor structure identified through exploratory factor analysis. In addition to the chi-squared statistic, given its sensitivity to sample size, the following adjustment indicators were used: the root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI) and standardized root mean square residual (SRMR). Good adjustment criteria were identified as values close to or lower than 0.08 in the case of RMSEA [61], values over 0.9 in CFI and TLI and those less than 0.05 in SRMR [62].
Cross-cultural validity is essential to ensure that an instrument measures a construct equivalently across different cultures, not only at the linguistic level but also at the conceptual and functional levels. To achieve this, psychometric evaluations are necessary, including confirmatory factor analysis to test factorial invariance at multiple levels (configural, metric and scalar). Accordingly, and following the recommendations of various authors [63, 64, 65], configural, metric and scalar invariance analyses were conducted by the country of origin to assess cross-cultural invariance.
Finally, to assess the internal consistency of the scale, the reliability of the factors was examined using McDonald’s Omega coefficient and the standardized Cronbach’s alpha. This procedure was applied to the samples from Chile and Costa Rica to compare the scale’s reliability across both countries, thereby complementing the assessment of cross-cultural validity.
4. Results
4.1 Digital capital composition based on exploratory factor analysis
Table 2 presents the factor solution obtained from the exploratory factor analysis. Three factors were obtained that group a total of 15 indicators. All of the items present a median of between 3.0 and 3.8 and asymmetry of less than 1, which indicates adequate distribution. In addition, based on Cronbach’s alpha, all of the factors present an acceptable internal consistency (greater than 0.7).
| No. | Item | Factorial load | Median | DE | Asymmetry | Alpha |
|---|---|---|---|---|---|---|
| Search and selection of information | 3.43 | 0.747 | 0.081 | 0.701 | ||
| 1 | Discard pages that do not report sources or references. | 0.67 | 3.46 | 1.106 | 0.465 | |
| 2 | Read the results obtained and use them to select and search for information. | 0.61 | 3.57 | 0.980 | 0.521 | |
| 3 | Define a type of source for searching for information. | 0.59 | 3.49 | 1.076 | 0.384 | |
| 4 | Select specific keywords and phrases to focus the search. | 0.57 | 3.50 | 1.121 | 0.317 | |
| 5 | Apply filters to the search, such as date, country or language. | 0.55 | 3.21 | 1.199 | 0.049 | |
| Production of information | 3.33 | 0.761 | 0.001 | 0.712 | ||
| 6 | Develop outlines to summarize information found on the Internet. | 0.78 | 3.05 | 1.127 | 0.007 | |
| 7 | Develop tables including information found online. | 0.73 | 3.00 | 1.022 | 0.072 | |
| 8 | Use a reference management tool to add citations or create a bibliography. | 0.53 | 3.19 | 1.241 | 0.226 | |
| 9 | Write your own ideas using information found on the Internet. | 0.49 | 3.82 | 0.915 | 0.554 | |
| Collaboration and exchange of information | 3.71 | 1.036 | 0.398 | 0.833 | ||
| 10 | Work with online documents to complete assignments as a team. | 0.86 | 3.74 | 1.153 | 0.603 | |
| 11 | Write documents simultaneously with others. | 0.82 | 3.77 | 1.198 | 0.445 | |
| 12 | Use online folders to share information or documents. | 0.78 | 3.65 | 1.202 | 0.516 | |
| 13 | Provide information that contributes to the development and progress of the work with my team. | 0.69 | 3.61 | 1.042 | 0.411 | |
| 14 | Organize your role and contribution within your work team. | 0.63 | 3.04 | 1.211 | 0.812 | |
| 15 | Share information that supports your teammates’ work. | 0.51 | 3.63 | 1.001 | 0.299 | |
Table 2.
Factor solution of the exploratory factorial analysis.
4.2 Confirmatory factor analysis with students from Chile
The confirmatory factor analysis conducted using data collected from higher education students in Chile indicated a poor model fit, with values falling outside the expected ranges: RMSEA = 0.085, SRMR = 0.079, CFI = 0.875 and TLI = 0.839. According to the covariance results, the item
Upon excluding these items, it was observed that the measurements of the
Figure 1.
Confirmatory factorial analysis of digital capital, Chile.
In addition, the covariance between the factors is over 0.5, and the factors that are most closely related are the information search and selection and information production factors. These results reveal the convergent validity between the factors, given that the greater the competency in information search and selection, the greater the competency in information production (r: 0.609) and the greater the competency of collaboration and exchange of information (r: 0,528). Furthermore, the greater the competency in information production, the greater the competency in collaboration and exchange of information (r: 0.511).
4.3 Confirmatory factor analysis with students from Costa Rica
The confirmatory factor analysis conducted using data from Costa Rican students indicated an acceptable model fit according to RMSEA = 0.059 and SRMR = 0.057, although the CFI = 0.845 and TLI = 0.849 values fell below the expected thresholds. As in the analysis with Chilean students, a low correlation was observed between the item “Organize your role and contribution within your work team” and the Collaboration and exchange of information factor (r = 0.209), leading to the decision to exclude the item.
With the revised model, an improved fit was obtained, as reflected in the following indices: RMSEA = 0.055, SRMR = 0.059, CFI = 0.917 and TLI = 0.909. Figure 2 shows that the factor loadings for the Information Search and Collaboration in Digital Environments factors are above 0.40. However, within the Digital Content Production factor, one item shows a factor loading below 0.30. Nevertheless, it is recommended to retain this item, as its exclusion does not lead to an improvement in model fit indices and would necessitate the removal of the entire dimension.
Figure 2.
Confirmatory factorial analysis of digital capital, Costa Rica.
4.4 Analysis of invariance
To examine cross-cultural validity, Table 3 presents the results for configural, metric and scalar invariance among students from Chile and Costa Rica. The values for configural invariance (CFI = 0.930; RMSEA = 0.067) indicate a good model fit. Similarly, the metric invariance shows comparable fit indices (CFI = 0.929; RMSEA = 0.065), with minimal differences relative to the configural model (ΔCFI = 0.001; ΔRMSEA = 0.002). Scalar invariance also demonstrates acceptable fit (CFI = 0.920; RMSEA = 0.066), with only slight changes compared to the metric model (ΔCFI = 0.009; ΔRMSEA = 0.001). These results support the conclusion that the factor structure of digital capital is equivalent across Chilean and Costa Rican students.
| CFI | RMSEA | Δ CFI | Δ RMSEA | AIC | ||||
|---|---|---|---|---|---|---|---|---|
| Configural | 405.48 | 102 | 0.930 | 0.067 | 44,522 | |||
| Metric | 420.42 | 111 | 0.092 | 0.929 | 0.065 | 0.001 | 0,002 | 44,519 |
| Scale | 466.60 | 120 | 0.584 | 0.920 | 0.066 | 0.009 | 0.001 | 44,547 |
Table 3.
Adjustment indices for invariance of digital capital by country of origin.
4.5 Reliability evidence
Once the unidimensional factorial structure was confirmed, the internal consistency of the scale was assessed. Reliability coefficients were higher in the Chilean sample (ω = 0.807; α = 0.802) and slightly lower in the Costa Rican sample (ω = 0.798; α = 0.794). Nonetheless, the results indicate that the digital capital scale demonstrates high reliability in both countries.
5. Discussion
The goal of this study was to design a theoretical construct and validate a digital capital measurement scale that allows for progress to be made on the study of digital gaps in Spanish-speaking higher education students. The results of the analyses conducted allow us to conclude that the unidimensional model acceptably represents the data observed and that the factorial structure does not vary based on country of origin.
As a result, it is possible to understand and measure digital capital as the accumulation of digital competencies as Ragnedda [25, 29] and Lee and Chen [41] have suggested. The competencies included correspond to searching and selecting information, producing information and collaborating on and exchanging information. The first two competencies have been considered in previous proposals [40, 41, 43], but, as we have noted, the third has only been considered in digital capital scale designs applied to the educational field [43]. Given that said competency involves frequent digital behaviors between higher education students [12, 19, 66], including it in the construct constitutes a contribution of this research.
Nevertheless, the dimension of collaboration and information exchange should be approached with caution in the application of future versions, gathering prior information on how this competency is developed within universities across different Latin American countries. In the specific case of this validation, it was necessary to remove the item “Organize your role and contribution within your work team” from the factorial solution for both Chile and Costa Rica. This outcome reflects, on one hand, the consistency in data comparability, given that the same item showed weak factor loadings in both samples. On the other hand, it reveals a skill that appears to be underdeveloped among the students who participated in the study.
From a different perspective, the competencies included in the theoretical construct are constituted as indispensable assets for acquiring new capital, given that in today’s societies, knowledge, productivity and competitiveness increasingly depend on the capacity to accumulate, generate and apply information effectively [1, 2]. As such, the construct of digital capital presented in this study is different from those that consider access, use of ICTs and/or digital abilities as contemporary indicators of cultural capital [16, 22] or as a subset of the economic, cultural and social forms of capital [38, 39]. This is not only due to the difference in the use of indicators but also because conceptually it is assumed that digital societies present new challenges and opportunities [3, 37], and that it is necessary to develop specific capitals to respond to them.
In this context, the concept of digital capital presented is consistent with the proposals that included digital competencies [26, 29, 41, 43] or abilities [39, 42, 50, 67] to measure digital capital. However, in contrast to previous experiences [26, 29, 36, 39, 42, 50] that contain a combination of factors (such as competencies and access), the concept developed in this study is exclusively focused on competencies measured through behaviors, similar to the proposals recently developed [28, 43].
These in turn correspond to the dimensions and sub-dimensions of frameworks used to define and evaluate digital competencies, especially in academic contexts like that of the ICILS study [47]. We decided not to include access in contrast to other proposals [26, 39, 42], because, as has been noted in previous studies [68, 69], this gap has decreased considerably among higher education students, which was corroborated by the data collected. This means that it would not seem to be a good indicator for measuring digital inequalities.
It is also important to note that the proposed items are different compared to scales of digital use [21, 70] or digital abilities [50, 71] used in research addressing gaps. We did not include items asking about activities or self-perception of skill levels. Instead, the questionnaire focused on behaviors. This decision allowed us to maintain the connection with the conceptualization of capital provided by Bourdieu, given that the digital capital described here is an accumulation of competencies [32] derived from the development of digital knowledge.
Finally, it is important to note that the results of invariance testing between countries allow for the determination of cross-cultural validity. These results are consistent with other initiatives that have previously measured digital skills in different countries or contexts [50, 53] and with research that has established that, despite the political, social, and educational differences among countries, it is possible to conduct valid comparisons of digital skill measurements across Spanish-speaking countries [72, 73].
6. Conclusions
Two conclusions can be drawn based on these results. First, the digital capital construct constitutes a contribution to the field, given that, in contrast to prior definitions, it covers a set of emerging behaviors with a singular value in today’s digital society and particularly in the educational field, inasmuch as, this tool can help to identify in which digital dimensions or practices students need help.
Second, it is a valid instrument for measuring digital capital among Spanish-speaking higher education students. Nevertheless, it is necessary to continue refining the scale by validating it with samples from other Latin American countries. In this context, it is important to highlight that higher education students do not constitute a homogeneous group, as there are notable differences among students across the region.
In this context, it is important to mention the limitations of the research, particularly those related to the sampling process. First, each country is represented by students from universities located in specific regions; nationally representative samples were not considered, which could have contributed to greater data variety and more robust analyses. Second, stratified sampling by field of study was not considered, which would be desirable to implement in the future in order to determine differences in digital capital according to academic discipline. Third, it would be beneficial to compare the factorial structure of the scale across a greater number of Spanish-speaking countries, particularly those with cultural and economic characteristics different from those of Chile and Costa Rica. Additionally, the items related to the content production dimension need improvement, as this dimension was reduced to only three items, making it essential to strengthen its measurement indicators.
In line with the above and considering the rapid advancement of information and communication technologies, it is also necessary to update the items to reflect behaviors associated with the use of artificial intelligence, particularly about information search, production and collaboration. In this context, several authors have made significant progress by proposing scales designed to measure these skills within academic settings. Specifically, and taking into account the contributions of scholars working in non-Latin American contexts, there are certain items that could be added and subsequently validated, such as: (i) I can create new results using the information or content I searched for; (ii) I can choose which of several AI services (platforms) are suitable for me, depending on the situation; (iii) I can use AI technology to find the information or content I need; (iv) I can use AI-based tools to search for educational material in my teaching field; (v) I am aware of various AI-based tools, which are used by professionals in my teaching field and (vi) I can use AI-based tools to better understand the contents of my teaching field.
Although further work is needed in the development of this instrument, the digital capital scale makes a significant contribution to the curricular development of universities by providing a valid and reliable tool to measure students’ digital competencies in diverse and cross-cultural contexts. This enables the identification of strengths and gaps in digital skills, which in turn facilitates the design of more relevant and up-to-date curricula aligned with current and future technological demands, such as the use of artificial intelligence. Furthermore, by allowing comparisons between different institutions and countries, this scale supports the development of educational strategies that promote digital equity and inclusion, thereby strengthening the comprehensive education of students in an increasingly digitalized world.
Acknowledgments
This work was funded by ANID-FONDECYT under project number 3180494.
Conflict of interest
The authors declare no conflict of interest.
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