Exploration of Youth’s Digital Competencies: A Dataset in the Educational Context of Vietnam

: The recent surge of the Fourth Industrial Revolution has set forth demands for a new generation of the labor force with a comprehensive set of skills to meet the standards of the global market. Despite widespread concerns about educational reforms and renovations to enhance the workforce capacity in terms of information and communication technology (ICT) skills, research into the digital proﬁciencies of students has been limited in Vietnam. This dataset contains 1061 observations on the digital competency level of 10th-grade students in 20 surveyed schools from ﬁve provinces in Vietnam. The investigation, joining frequentist and Bayesian analyses, aims to provide valuable insights into the current state of children’s attitudes, behaviors, competency levels, and use of ICT within the Vietnamese educational context. The values of the dataset lie in its proposed scientiﬁc framework for replication in multiple regions and contexts as well as the feasibility of categorical regression techniques together with Bayesian statistics for hierarchical regression analysis. Dataset: The dataset is submitted and will be published as a supplement to this paper. V.-P.L.; investigation, D.-L.D., H.-N.N., H.-K.T.N; resources, A.-V.L., D.-Q.P., D.-L.D.; data curation, D.-Q.P., H.-N.N., T.-H.D.; writing—original draft preparation, P.-H.H., D.-L.D., H.-N.N., T.-T.V., H.-K.T.N., and M.-T.H.; writing—review and editing, M.-T.H., P.-H.H., T.-T.V., H.-K.T.N.; visualization, D.-L.D., V.-P.L.; supervision, A.-V.L., Q.-H.V.; project administration, D.-Q.P., M.-T.H., and Q.-H.V.

The DKAP survey tool has been assessed as reliable and validated to measure digital citizenship competencies under the DKAP Framework. A total of 5129 responses from children aged 15 from four countries-Bangladesh, Fiji, South Korea, and Vietnam-have been obtained using the instrument within the scope of a comparative investigation study of digital citizenship across the pilot countries.
The Vietnam Institute of Educational Sciences (VNIES) became the focal point of the Ministry of Education and Training to carry out the project component in Vietnam. VNIES signed the contract with UNESCO Hanoi (N 0 4500363176) to conduct the survey in September 2018 in five provinces within the country. The survey questionnaire of the project in Vietnam was translated into Vietnamese from the English version of the DKAP framework.
The dataset contains responses by 1061 15-year-old school students regarding their digital competencies. The questionnaire consists of 117 multiple-choice questions, most of which require the respondents to choose one single answer out of the provided options, while some of the questions ask for more than one answer. Questions and answers were treated as discrete and continuous variables and encoded according to the coding instructions provided by UNESCO (see the dataset). The questions were divided into two groups: group (1), contextual questions; and group (2), digital competence questions. Group 1 contains 33 items asking for students' ICT experiences along with their demographic information. Group 2 contains 84 items concerning students' ICT competencies across five different domains.

Group (1) Personal Background Questions
The 33 question items in group (1) cover three domains: (1) student personal background (eight items); (2) access and usage of digital devices (18 items); and (3) socioeconomic status (SES) (seven items). The distribution of answers for domain (1) questions is presented in Table 1. The percentage of female participants is slightly higher than male participants (by 6.2%). Nearly all (99%) of the students were born in 2003, and all were in Grade 10. In the Vietnamese 12-grade education system, these students were in the first year of senior high school.
The question of academic expectation asks about the highest expected education level. The responses were encoded as variable 'F6'. Statistics show that the majority of students wished to complete post-secondary (36%) and masters/doctoral level (33.5%). Distributions of responses to F6 by gender can be found in Figure 1.

Domain (2): Access and Usage of Digital Devices
Regarding students' access and usage of digital devices, the first question in domain (2) on students' "experience of using digital devices" yields four values: 'never', 'less than 1 year', '1-2 years', '3-4 years', and 'more than 5 years' ('G1'). The other questions in this domain collect information about the frequency of students' Internet access from a digital device ('G2'); the location from which they are connected to the Internet ('G3'), the types of digital devices used for Internet connection from home ('G4'), school ('G5'), or a local community access point ('G6'); the type of Regarding students' access and usage of digital devices, the first question in domain (2) on students' "experience of using digital devices" yields four values: 'never', 'less than 1 year', '1-2 years', '3-4 years', and 'more than 5 years' ('G1'). The other questions in this domain collect information about the frequency of students' Internet access from a digital device ('G2'); the location from which they are connected to the Internet ('G3'), the types of digital devices used for Internet connection from home ('G4'), school ('G5'), or a local community access point ('G6'); the type of Internet connection at home ('G7'), school ('G8'), or a local community access point ('G10'); whether students can access the Internet at a public venue or not ('G9'); people who provided instructions on computers and the Internet ('G11' and 'G12'); the purpose and use of Internet connections ('G13'-'G16'); and experiences with coding and software development ('G17' and 'G18').
The distribution of answers for domain (2) questions is presented in Table 2. Almost all the students had Internet coverage at home (97.2%).   The responses to questions on the purposes of computer and Internet usage were encoded as continuous variables 'G13', 'G14', 'G15', and 'G16'. Statistics indicate that the modal value for 'G13' is 1-2 h of computer and Internet usage per day for school study purposes. Figure 2 depicts the distribution of values for variable 'G14' about the amount of time spent online or using computer by gender. The horizontal axis refers to the provided different time lengths. The blue columns represent the number of female students, and the red columns represent the number of male students. It can be seen that a large number of students spent between one and two hours online for personal study purposes (55%). It also seems that female students preferred spending more hours online or using a computer than their male counterparts.  Questions regarding socioeconomic status in domain (3) concerned issues such as the highest education level of students' parents ('H2' and 'H3'), level of access to physical ('H4') and academic resources ('H5'), and level of support by others ('H6' and 'H7'). The distribution of answers for domain (1) questions is presented in Table 3. Questions regarding socioeconomic status in domain (3) concerned issues such as the highest education level of students' parents ('H2' and 'H3'), level of access to physical ('H4') and academic resources ('H5'), and level of support by others ('H6' and 'H7'). The distribution of answers for domain (1) questions is presented in Table 3.

H7
Level of encouragement from others to explore or learn things on the Internet Regarding the level of help and support from others, less than a quarter of parents/caregivers were perceived as being highly concerned for their children's cyber safety ('H6_1'). The level of encouragement from parents, teachers, peers, and siblings for the students' online learning activities also seem to be low ('H7').

Group (2) Competency Questions
The purpose of group (2) questions is to learn about competency level in using digital technology and the capacity to manage potential risks caused by digital technology. This group comprises five domains and corresponding component capacities: Digital Literacy, Digital Safety and Resilience, Digital Participation and Agency, Digital Emotional Intelligence, and Creativity and Innovation (see the dataset).
The Digital Literacy domain consists of 14 questions assessing the use of tools and digital information: for example, the ability to use software and digital devices and exploit digital information in different contexts. Assessing scale is in the form of a four-point Likert scale measuring the extent to which students can exploit tools and digital information. The visual distribution of responses for this variable is displayed in Figure 3a. The modal option is 'agree a little' to the ability to use digital tools and information, followed by an almost 30% proportion of the respondents claiming to 'agree a lot', meaning highly confident in their ability to handle digital tools and information. Besides, the distribution of gender in each choice is relatively homologous. The mean scores by gender in the Digital Literacy domain is around 3.1 (Figure 3b).
The Digital Literacy domain consists of 14 questions assessing the use of tools and digital information: for example, the ability to use software and digital devices and exploit digital information in different contexts. Assessing scale is in the form of a four-point Likert scale measuring the extent to which students can exploit tools and digital information. The visual distribution of responses for this variable is displayed in Figure 3a. The modal option is 'agree a little' to the ability to use digital tools and information, followed by an almost 30% proportion of the respondents claiming to 'agree a lot', meaning highly confident in their ability to handle digital tools and information. Besides, the distribution of gender in each choice is relatively homologous. The mean scores by gender in the Digital Literacy domain is around 3.1 (Figure 3b). Similarly, the other four domains (Digital Safety and Resilience, Digital Participation and Agency, Digital Emotional Intelligence, and Creativity and Innovation) also employ a four-point Likert scale to evaluate the corresponding capacities. Figure 4 illustrates the distributions of responses in each of the domains.
The Digital Safety and Resilience domain includes 18 questions examining students' understanding about digital rights, privacy protection, well-being, and risk management ability in Similarly, the other four domains (Digital Safety and Resilience, Digital Participation and Agency, Digital Emotional Intelligence, and Creativity and Innovation) also employ a four-point Likert scale to evaluate the corresponding capacities. Figure 4 illustrates the distributions of responses in each of the domains.   Figure 5a presents the distribution of responses in the Digital Participation and Agency domain with 12 questions evaluating students' reactions and behaviors regarding collaboration as well as engagement in the digital environment. The statistics show that most of the responses are in the range of "disagree a little" to "agree a little". Figure 5b demonstrates that girls scored slightly higher than boys in this domain. The Digital Safety and Resilience domain includes 18 questions examining students' understanding about digital rights, privacy protection, well-being, and risk management ability in the digital world. Figure 4a is the histogram of mean scores, which suggests that most of the students felt that they understood their rights, and knew how to protect their privacy and react to potential risk in the digital world. Figure 4b shows a small discrepancy in responses between girls and boys in this domain. Figure 5a presents the distribution of responses in the Digital Participation and Agency domain with 12 questions evaluating students' reactions and behaviors regarding collaboration as well as engagement in the digital environment. The statistics show that most of the responses are in the range of "disagree a little" to "agree a little". Figure 5b demonstrates that girls scored slightly higher than boys in this domain.
The distribution of students' answers to questions in the Digital Emotional Intelligence domain is displayed in Figure 6a. This domain consists of 16 questions aiming to assess students' interpersonal skills and awareness when joining the digital world (i.e., their use of social networking sites and real-time chatting apps). The histogram of mean scores demonstrates that most of the answers fall into the range of "agree a little" to "agree a lot", meaning that the students showed firm understanding and awareness of legitimate cyber behaviors. The mean score in this domain is not high (around three), with a narrow gap between answers by boys and girls (Figure 6b). The distribution of students' answers to questions in the Digital Emotional Intelligence domain is displayed in Figure 6a. This domain consists of 16 questions aiming to assess students' interpersonal skills and awareness when joining the digital world (i.e., their use of social networking sites and real-time chatting apps). The histogram of mean scores demonstrates that most of the answers fall into the range of "agree a little" to "agree a lot", meaning that the students showed firm understanding and awareness of legitimate cyber behaviors. The mean score in this domain is not high (around three), with a narrow gap between answers by boys and girls (Figure 6b). The Creativity and Innovation domain contains 11 questions measuring students' ability to develop creative digital products and present oneself in the digital world. Figure 7a suggests that most of the responses have the value of "disagree a little". This means that the majority of surveyors were slightly doubtful of their originality and creativeness in manipulating digital resources on online platforms. The Creativity and Innovation domain contains 11 questions measuring students' ability to develop creative digital products and present oneself in the digital world. Figure 7a suggests that most of the responses have the value of "disagree a little". This means that the majority of surveyors were slightly doubtful of their originality and creativeness in manipulating digital resources on online platforms. It is noted that questions 15 to 18 concerning digital resilience are in multiple-choice instead of Likert-scale form (see the dataset). The descriptive statistics of questions 15 to 18 are listed in Table  4.   Table 4.

Potential Research Questions
Use of the Internet or digital devices at home is a vital factor influencing primary students' ICT literacy [13]. Specifically, a study in German primary schools suggested a lack of parental concern for the online behaviors of their children from lower socioeconomic backgrounds [14]. Moreover, it is not easy to obtain the goal of digital equity among students from different backgrounds [15]. In general, a higher level of self-efficiency is associated with a better level of self-perceived digital competencies [16]. Regarding gender, there are differences in the basic digital competencies of male and female university students [17]. Drawing on the dataset, we present potential research questions in the following list. • What are the background factors that could affect students' digital competency levels? Are there any differences in the digital competency levels of male and female school students?

Research Framework
This research is theoretically based on Bronfenbrenner's bio-ecological model, which describes a child's maturity in interactions with multiple levels of sociodemographic, cultural, and societal elements that constitute his or her community [18]. In particular, the model proposes four layers of the environment with a respective impact on a child's cognitive growth.
As illustrated in Figure 8, the innermost circle represents a microsystem that contains the developing individual together with their personal and closest ties. The next layer is a mesosystem involving the interdependence of the microsystems with which the developing individual actively interacts (e.g., the child's interrelationship between home and school environments). This is accommodated in an ecosystem with contexts having indirect and distant effects on the developing child. The outermost layer macrosystem encompasses systematic cross-cultural compatibilities together with philosophies or ideologies that reinforce the structure. Given the scarcity and underdevelopment of theoretical explanations for children's development of digital competencies, Bronfenbrenner's bio-ecological model serves as a useful framework by offering a child-centered approach to examine children's behaviors, knowledge, or attitudes relating to ICT, considering multiple layers of social impacts, and illustrated in the form of concentric circles of family, schools, or community and culture.
The framework employed in this study proposes three sets of interconnections: o Personal level within the microsystem; o Social mediation level, primarily concerning home, school system, and peer networks within the mesosystem; and o National level where the country is the subject of analysis, and the macro levels of socioeconomic classification, systems of regulation, and cultural values act as influential factors.
The Conference on Digital Citizenship Education in Asia-Pacific and the subsequent experts' meeting have proposed a detailed framework of digital citizenship domains, proficiencies, and performance indicators to comprise a wide parameter of essential competencies for a digital citizen to adapt to, develop, and serve the digital community in the 21st century. An itemized description of the framework is provided in Table A1, Appendix A. Definitions of the five suggested domains are listed in Table 5 below.

Microsystem -Home, School
Person-Digital Competenties Given the scarcity and underdevelopment of theoretical explanations for children's development of digital competencies, Bronfenbrenner's bio-ecological model serves as a useful framework by offering a child-centered approach to examine children's behaviors, knowledge, or attitudes relating to ICT, considering multiple layers of social impacts, and illustrated in the form of concentric circles of family, schools, or community and culture.
The framework employed in this study proposes three sets of interconnections: Personal level within the microsystem; Social mediation level, primarily concerning home, school system, and peer networks within the mesosystem; and National level where the country is the subject of analysis, and the macro levels of socioeconomic classification, systems of regulation, and cultural values act as influential factors.
The Conference on Digital Citizenship Education in Asia-Pacific and the subsequent experts' meeting have proposed a detailed framework of digital citizenship domains, proficiencies, and performance indicators to comprise a wide parameter of essential competencies for a digital citizen to adapt to, develop, and serve the digital community in the 21st century. An itemized description of the framework is provided in Table A1, Appendix A. Definitions of the five suggested domains are listed in Table 5 below.

Digital Literacy
The ability to seek, critically evaluate, and use digital tools and information effectively to make informed decisions

Digital Safety and Resilience
The ability to understand how to protect oneself and others from harm in a digital space

Digital Participation and Agency
The ability to equitably interact, engage, and positively influence society through ICT

Digital Emotional Intelligence
The ability to recognize, navigate, and express emotions in one's digital intrapersonal and interpersonal interactions

Digital Creativity and Innovation
The ability to express and explore oneself through the creation of content using ICT tools The framework takes a rights-based and child-centered approach aligned with the commonly endorsed Convention on the Rights of the Child, which constitutes a common reference on human rights standards for children.

Data Collection
The research team strictly followed UNESCO's procedure for the survey: The sample geographical locations are indicated in the map in Figure 9. In total, the survey covered 1061 high school students (See Table A2, Appendix A) from 20 schools located in five provinces and cities: Lao Cai, Hanoi, Danang, Lam Dong, and Can Tho.

Data Analysis
Raw data gathered from the questionnaire were entered into a spreadsheet at data.csv (see the dataset). Then, the data were processed and saved in CSV format for analyses using R statistical software (v3.5.3). Both frequentist and Bayesian statistics approaches were employed in the data analysis process.

Frequentist Analysis
Since the majority of variables in the dataset are categorical, most of the responses and predictor variables are discrete; thus, it's appropriate to use logistic regression model for data analysis [19,20]. In the logistic regression model, we use the two following equations: In which x is the independent variable, and π j (x) = P(Y = j x) is the corresponding probability. Therefore, π j = P Y ij = 1 , with Y as the dependent variable.
questionnaire; (2) Conducting the pilot test for the survey questionnaire at two schools in Hanoi and making necessary amendments in August 2018; (3) Contacting the target school administrators and coordinators and carrying out administrative work for the investigation; (4) Implementing the survey with the support of school coordinators in September 2018 at 20 schools across provinces in Vietnam; (5) Cleaning and encoding the data from 18 September to 1 October 2018, according to the codebook and coding instructions (see the dataset) provided by UNESCO Bangkok.
The sample geographical locations are indicated in the map in Figure 9. In total, the survey covered 1061 high school students (See Table A2, Appendix A) from 20 schools located in five provinces and cities: Lao Cai, Hanoi, Danang, Lam Dong, and Can Tho. The second equation estimates the probability of each item of dependent variables: Besides, the data can be analyzed by a linear regression model for the numerical variables. The general equation of the linear equation is: where Y is a continuous variable; and the independent variables X i can be concrete, categorical, or continuous.
The linear regression method is applied with the outcome variable being digital resilience (from 'B15' to 'B18'), the father's highest level of education ('H2'), the student's expectation of highest  Table 6. Examples of the code on R that were used to come up with the results in Table 6 are presented below: >t=file.choose() >data=read.csv(t, header=T, na.strings="99") >attach(data) > ds = lm(mean_b3~factor(g1) + factor(f6) + factor(h2), data=data) The following example in Table 7 presents the relationship between time spent using digital devices, biological sex, and the student's digital emotional intelligence.

Bayesian Analysis
The Bayesian statistics approach will also be used to examine the dataset in this section. A hierarchical regression model of the amount of experience that students have with using digital devices ('G1') according to their schools and sex was developed by employing R statistical software and BayesVL package (v0.7.5), which is available in [21]. Similar applications of Bayesian statistics can be found in [22,23]. The Bayesian approach is strong in visually demonstrating the results and the distributions of the coefficients. Moreover, the robustness of the model is tested by analysis of the sensitivity of the model to prior change. Its credibility is evident when the model does not show sensitivity to adjustment of the prior [24][25][26][27].
The mathematical formula of the model is as follows: In which j = 20 schools, and G1 is the student's experiences in using digital devices: 1 = Never; 2 = Less than 1 year; 3 = 1-2 years; 4 = 3-4 years; and 5 = More than 5 years.
Examples of codes that were used to command the BayesVL package to construct the model are as follows: Box 1 # Design the model model <bayesvl() model <-bvl_addNode(model, "G1", "norm") model <-bvl_addNode(model, "sex", "norm") model <-bvl_addNode(model, "schoolid", "norm") model <-bvl_addArc(model, "schoolid", "G1", "varint") model <-bvl_addArc(model, "sex", "G1", "slope") # Generate the stan code for model model_string <-bvl_model2Stan(model) cat(model_string) # Fit the model fit <-bvl_modelFit(model, dkap_data, warmup = 2000, iter = 20000, chains = 4, cores = 1) Moreover, the STAN codes that were generated by the BayesVL package for the model sampling and parameter learning are: The results from the hierarchical regression model are as in Table 8.  The posterior coefficients are shown in Figure 10: The posterior coefficients are shown in Figure 10: The posterior distribution of all the coefficients is as in Figure 11. The mean of the mu_alpha is around 4.2, which shows a high level of usage of technological devices. The posterior distribution of all the coefficients is as in Figure 11. The mean of the mu_alpha is around 4.2, which shows a high level of usage of technological devices. The posterior distribution of all the coefficients is as in Figure 11. The mean of the mu_alpha is around 4.2, which shows a high level of usage of technological devices. In the model, the correlation coefficients' posterior distributions are presented in Figure 12. The diagonal boxes present the posterior distributions for individual coefficients: beta_sex, sigma_e0, mu_alpha, and sigma_alpha. The simulated pairs of each coefficient are shown in the off-diagonal boxes. All satisfy the standard distributions. The log posterior of the model is shown in Figure 13: The log posterior of the model is shown in Figure 13:   Figure 14 shows the comparison among surveyed schools in the digital device usage experience of students. The overall usage of the digital device is above average, and it is notable that many schools from more developed cities such as Hanoi or Danang show a low level of digital device usage.  Figure 15 explains the correlation between sex and the usage of technological devices based on the slope coefficient beta_sex. The sex and usage of digital devices show low correlation, but female students demonstrate a slightly higher level of usage than their male counterparts because the value of the coefficient is smaller than zero.    Figure 15 explains the correlation between sex and the usage of technological devices based on the slope coefficient beta_sex. The sex and usage of digital devices show low correlation, but female students demonstrate a slightly higher level of usage than their male counterparts because the value of the coefficient is smaller than zero.

Conclusions and User Notes
Our dataset offers comprehensive descriptive statistics yielding significant insights regarding digital citizenship competencies in the Vietnamese educational context, specifically school students' perceptions, proficiency levels, and behavioral use of ICT. This research area has rarely been studied in the field of Social Sciences and Humanities, despite potential challenges concerning professional

Conclusions and User Notes
Our dataset offers comprehensive descriptive statistics yielding significant insights regarding digital citizenship competencies in the Vietnamese educational context, specifically school students' perceptions, proficiency levels, and behavioral use of ICT. This research area has rarely been studied in the field of Social Sciences and Humanities, despite potential challenges concerning professional development facing developing countries such as Vietnam in the age of Industry 4.0. With over 100 question items collecting information across the five domains-Digital Literacy, Digital Safety and Resilience, Digital Emotional Intelligence, Creativity and Innovation, and Digital Participation and Agency-the dataset contains values of multiple variables, both categorical and continuous, hence allowing potential diverse methodologies of in-depth analyses and the strict control of variables.
The richness of our dataset would foster further research on multifaceted domains of digital competencies in adolescents. Promising grounds for future investigations include the effects of school-related factors such as curriculum, teaching practices, syllabi or assessment criteria and format, as well as non-educational factors concerning demographic backgrounds, daily time-spending routines, or online behavioral activities. Research into these areas is critical, as ICT skills have been identified as one of the major barriers to students and teachers' readiness for STEM (Sciences, Technology, Engineering, and Mathematics) education [28]. Scientific findings regarding the determinants of both the cognitive and the non-cognitive attributes of the ICT competency of students would comprehensively inform future decisions and suggestions for policy development, particularly in the education sector in developing countries with similar socioeconomic structures to Vietnam [29].
In addition to presenting the dataset, this article also explores statistical methods for data analysis, which is categorical data in this dataset. Traditionally, the frequentist approach is used for data analysis. However, as the scientific community is debating over the traditional approach, due to the manipulation of statistical significance and other misconducts such as stargazing, p-hacking, or HARKing [30], we also introduce the application of Bayesian statistics for hierarchical regression analysis. The employment of both frequentist and Bayesian approaches are expected to strengthen the credibility and soundness of scientific results produced from the dataset, which would pique the interests of the scientific community and policymakers.
The values of this dataset are beyond the instant analyses of data, considering its high replicability of methodology and the survey framework in different regions and contexts. As stated earlier in this text, the study was originally designed to make a cross-national comparison of data in four countries: Bangladesh, South Korea, Vietnam, and Fiji. The findings derived from this dataset would be more generalizable if the target sample is extended to include more observations from students at different levels of study rather than limited to only 10th graders. A more comprehensive sample, which is entirely feasible in the future, would allow interesting cross-regional and cross-generational findings on a panoramic scale.
Therefore, replicating the survey framework to yield comparable datasets would contribute to a cross-boundary database with immense scientific implications. Knowledge sharing, open access to data and information are also aligned with the current movements in the academic world that resulted from better communication and connection concerning international collaboration in research, transparency of data processing, and Open Science [31,32]. It is not unusual nowadays that studies with groundbreaking findings are attained by large research groups from all over the world, such as the picture of the black hole [33] or the large dataset of societies [34]. All these changes will ultimately address the global sustainable development goals of United Nations. This is also the original aim of this investigation by UNESCO, and the reason why the organization approved the dissemination and access of this dataset.

Supplementary Materials:
The following are available online at http://www.mdpi.com/2306-5729/4/2/69/s1, Figure S1: title, Table S1: title, Video S1: title.    3.1.1 The child is able to use digital tools to interact and share information and data with peers and other children from a variety of background and cultures. 3.1.2 The child is able to use digital tools to interact and share information and data with adults from a variety of background and cultures. 3.1.3 The child is able to use digital tools to work together with peers and other children to achieve a common goal.
3.1.4 The child is able to use digital tools to work together with adults to achieve a common goal.
3.2 Civic Engagement: The ability and willingness to recognize, seek, and act on opportunities to positively influence local and global communities online and/or offline through appropriate digital technologies.

3.2.1
The child is able to use ICT to discuss political and social issues with other people online 3.2.2 The child is able to use ICT to be involved in activities, associations, and movements on social and political issues.
3.2.3 The child is able to use ICT with the intention to influence society, locally or globally.

3.2.4
The child believes that their involvement contributed to a better world.
3.3 Netiquette: Demonstrate ethical and courteous behavior to inform choices in interacting and engaging in different digital environments with different audiences.
3.3.1 The child acts with courtesy in their interaction with others while using digital tools. 3.3.2 The child demonstrates respect for others' rights through their online behavior. 3.3.3 The child demonstrates non-discriminatory behavior that is also gender and culturally sensitive.
Digital Emotional Intelligence 4.1 Self-Awareness: Ability to explain one's moods, emotions, drives, and how these affect oneself and others in the digital world through introspection. 5.6.1 The child is able to utilize digital platforms to explore, experiment, and generate ideas. 5.6.2 The child is able to use digital platforms to creatively represent digital and real-life identities. 5.6.3 The child is able to use creative digital formats to express ideas and connect with others.