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

Round  1

Reviewer 1 Report

Rather table heavy - Table 1 seems excessive as a table. Perhaps re-present that as a collection of smaller tables, or visually (and refer readers to underlying data set).

conversely, some of the graphs (fig 4 and 9 in particular) seem of interest, but are presented quite small and would benefit from more explanation, discussion and analysis.

Author Response

Letter of detailed responses to Reviewer 1

Dear Sir/Madam,

Thank you very much for spending a great amount of time and effort you have put in to reviewing our manuscript. Your detailed comments have helped us improve the quality of our paper.

We have addressed your points in our revised version. Please notice that in the revised paper, the parts that are highlighted in yellow is for correction on the old text, the parts highlighted in green is written anew. Below are our answers to your comments (in italic). Also, the line numbers in the text refer to the revised paper.

Rather table heavy - Table 1 seems excessive as a table. Perhaps re-present that as a collection of smaller tables, or visually (and refer readers to underlying data set).

We have divided Table 1 in three different tables that describe the distributions of students according to their personal background (Table 1), access and usage of digital devices (Table 2), and socioeconomic status (Table 3). Moreover, section 2.1 was also restructured with three new sections for each domain: (1) Student personal background; (2) Access and usage of digital devices; and (3) Socioeconomic status (SES).

Conversely, some of the graphs (fig 4 and 9 in particular) seem of interest, but are presented quite small and would benefit from more explanation, discussion and analysis.

We have provided higher resolution images to better present the content of all the figures.

We have separated figure 4 into four new figures as follows:

Figure 4. Histogram of (a) Distribution of mean score and (b) Mean scores by gender in Digital safety and resilience domain.

Figure 5. Histogram of (a) Distribution of mean score and (b) Mean scores by gender in Digital participation and agency domain.

Figure 6. Histogram of (a) Distribution of mean score and (b) Mean scores by gender in Digital emotional intelligence domain.

Figure 7. Histogram of (a) Distribution of mean score and (b) Mean scores by gender in Creativity and Innovation domain.

Each figure was also explained in a short paragraph. For Figure 4, see ….:

Figure 4a is the histogram of mean scores which suggests that most of the students felt they understand their rights, know how to protect their privacy and react to potential risk in the digital world. Figure 4b shows small discrepancy in responses between girls and boys in this domain.

For Figure 5: 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.

For Figure 6: Distribution of students’ answers to questions in 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 behaviours. The mean score in this domain is not high (around 3) with a narrow gap between answers by boys and girls (Figure 6b).

For Figure 7: Figure 7a suggests that most of the responses have the value of “disagree a little”. This means the majority of surveyors were slightly doubtful of their originality and creativeness in manipulating digital resources on online platforms.

For Figure 9, we have added a short paragraph to explain and discuss the figure:

The diagonal boxes present the posterior distributions for individual coefficients: beta_sex, sigma_e0, mu_alpha, sigma_alpha. The simulated pairs of each coefficient are shown in the off-diagonal boxes. All satisfy the standard and technical distributions.

We appreciate the hard work and time that you have spent on this manuscript. Your comments and suggestions have helped us improve the quality of our paper. We hope that the revised paper has met your requirements.

Please accept our sincere thanks for your great contributions to the improvement of higher studies on education and the overall advancement of sciences in the world.

Shall you have further comments, we look forward to hearing from you.

Best regards,

Reviewer 2 Report

It has great data on digital competencies of youthVietnamese.

I did not deny the value of this paper that introduce the data.

But who can use these data? and why people who can not use or analyse these data have to know and read this paper? 

Every nation, institute or research has his/her own special data that will cover his/her country.

But all data could not be introduced like this.

I can not agree that this submitted paper is published.

So, how about submitting it at journal of your courtryt?

Than, you can submit the detailed result of these data at this journal.

Of course, it is my opinion.

Thanks.

Author Response

Letter of detailed responses to Reviewer 2

Dear Sir/Madam,

Thank you very much for spending a great amount of time and effort you have put in to reviewing our manuscript. Your detailed comments have helped us improve the quality of our paper.

We have addressed your points in our revised version. Please notice that in the revised paper, the parts that are highlighted in yellow is for correction on the old text, the parts highlighted in green is written anew. Below are our answers to your comments (in italic). Also, the line numbers in the text refer to the revised paper.

It has great data on digital competencies of youth Vietnamese. I did not deny the value of this paper that introduce the data.

Thank you very much for your comments.

But who can use these data? and why people who cannot use or analyse these data have to know and read this paper? 

We appreciate your questions which inquire about the originality and usability of our dataset. We would like to address your points as follow:

Our dataset is valuable in three ways:

i)                    Universality in study design and survey framework:

Methodology and conceptualisation of data are also considered scientific assets. The study design and survey framework are highly accessible and replicable at this stage as well as in the future for use in other regions or contexts, allowing cross regional and national comparison of results. The publication of this dataset would encourage dissemination of datasets from other comparable investigations, which would greatly contribute to the scientific community. We believe that this is also the goal of Data as well as the global scientific information network in general.

ii)                   Methods of data analysis—Bayesian and frequentist approaches:

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 [28], 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.

iii)                 Open Data and Open Sources:

We are committed to make our data and source codes completely open. Our research’s approach is in line with current progress in the scientific community concerning Open Science where Open Data and Open Sources are emphasized. This is also the original aim of this investigation by UNESCO under the contract N⁰ 4500363176 and the reason why the organisation approved upon the dissemination and access of this dataset. Besides, knowledge sharing and transparency of data processing are vital to curbing the reproducibility crisis that is plaguing social sciences and humanities [1], we believe this is also one of the missions of Data.

In the revised version of the manuscript, we have addressed the originality and usability of our dataset in the “Conclusion and User Notes” section (See Line 430 - 455):

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 [2], 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 instant analyses of data, considering its high replicability of methodology and 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. Findings derived from this dataset would be better generalisable if the target sample is extended to include more observations from students at different levels of study rather than limited to only 10^th graders. A more comprehensive sample, which is entirely feasible in the future, would allow interesting cross-regional and cross-generational findings from a panoramic scale.

Replicating the survey framework to yield comparable datasets would, therefore, 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 which resulted from better communication and connection, concerning international collaboration in research, transparency of data processing and Open Science [3,4]. 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 [5] or the large dataset of societies [6]. 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 organisation approved upon the dissemination and access of this dataset.

Every nation, institute or research has his/her own special data that will cover his/her country. But all data could not be introduced like this.

Thank you for your comment which we also agree with. However, it is becoming a common practice nowadays that scientific research should target big data, allowing cross national database and comparison. Knowledge sharing and open access to data sources would potentially yield immensely powerful scientific findings. All these changes will ultimately address the global sustainable development goals of the United Nations. The feasibility of these goals would be limited without publication of datasets like this, as would cross national comparisons of empirical evidence and background for meta-analysis.

I can not agree that this submitted paper is published.

So, how about submitting it at journal of your courtryt?

Than, you can submit the detailed result of these data at this journal.

Of course, it is my opinion.

Thanks.

- Thank you for your feedback. We are also working to publish the detailed results and findings from this dataset. However, given our scientific goals and viewpoint as explained earlier, we believe:

a) The publication of findings and implications from different parts or the whole of this dataset should not be the reason for suspending the publication of the dataset  

b) We will continue to analyze and explore findings from this dataset as two of the author are Guest Editors of the Special Issue "Academic Contributions to the UNESCO 2019 Forum on Education for Sustainable Development and Global Citizenship" of Sustainability by MDPI:

https://www.mdpi.com/journal/sustainability/special_issues/UNESCO_2019

c) On the other hand, we also hope to expand opportunities for collaboration in research as well as confirm the potential of open data application and publication by having this article published. Another similar case is our newly published dataset of healthcare and insurances through which we received invitation for collaboration from Europe within one week from the publication date on Data .

Once again, we value your comments and feedback and retain our opinion about knowledge sharing and promotion of open science and open data [3]. We have revised and resubmitted the manuscript and hope to receive your positive assessment.

We appreciate the hard work and time that you have spent on this manuscript. Your comments and suggestions have helped us improve the quality of our paper. We hope that the revised paper has met your requirements.

Please accept our sincere thanks for your great contributions to the improvement of higher studies on education and the overall advancement of sciences in the world.

Shall you have further comments, we look forward to hearing from you.

Best regards,

References:

1.            Ball, P. High-profile journals put to reproducibility test. Nature 20198, 10.1038/d41586-018-06075-z, doi:10.1038/d41586-018-06075-z.

2.            Vuong, Q.H.; Ho, M.T.; La, V.-P. ‘Stargazing’ and p-hacking behaviours in social sciences: some insights from a developing country. European Science Editing 2019, 45.

3.            Vuong, Q.H. (2017). Open data, open review and open dialogue in making social sciences plausible. Availabe online: http://blogs.nature.com/scientificdata/2017/12/12/authors-corner-open-data-open-review-and-open-dialogue-in-making-social-sciences-plausible/ (accessed on May 8).

4.            Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; da Silva Santos, L.B.; Bourne, P.E., et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 2016, 3, 160018, doi:10.1038/sdata.2016.18.

5.            Collaboration, E.H.T. First M87 Event Horizon Telescope results. I. The shadow of the supermassive black hole. Astrophysics Journal Letters 2019, 875, L1, doi:10.3847/2041-8213/ab0ec7.

6.            Whitehouse, H.; François, P.; Savage, P.E.; Currie, T.E.; Feeney, K.C.; Cioni, E.; Purcell, R.; Ross, R.M.; Larson, J.; Baines, J., et al. Complex societies precede moralizing gods throughout world history. Nature 2019, 568, 226-229, doi:10.1038/s41586-019-1043-4.

Reviewer 3 Report

First, I want to commend all of you for attempting to make the data set publicly available. That said, the manuscript is very well written. I have a few suggestions that you might want to consider:

+ Please consider offering a few words on Vietnam’s 4^th industrial age. Not everyone will be familiar with this era of the country, and a brief explanation would offer a foundation by which to frame the need to have conducted this data collection effort.

+ While those consuming the manuscript and respective data set can figure out how the male/female data is coded (1 or 2), please consider labeling these values as male or female (instead of 1 and 2) in Figures 1 and 2 and in the respective text (see L94, L117).

+ Also, I’m assuming the 99 values in the data set are missing data? Please consider offering the codes in the data set so that the enumerated values are appropriately mapped. While redundant in some respects giving the manuscript discussion and findings, please simply make it clear (e.g., 1=male, 2=female, etc.).

Author Response

Letter of detailed responses to Reviewer 3

Dear Sir/Madam,

Thank you very much for spending a great amount of time and effort you have put in to reviewing our manuscript. Your detailed comments have helped us improve the quality of our paper.

We have addressed your points in our revised version. Please notice that in the revised paper, the parts that are highlighted in yellow is for correction on the old text, the parts highlighted in green is written anew. Below are our answers to your comments (in italic). Also, the line numbers in the text refer to the revised paper.

First, I want to commend all of you for attempting to make the data set publicly available. That said, the manuscript is very well written. I have a few suggestions that you might want to consider:

+ Please consider offering a few words on Vietnam’s 4^th industrial age. Not everyone will be familiar with this era of the country, and a brief explanation would offer a foundation by which to frame the need to have conducted this data collection effort.

Thank you for your suggestions. We have included new paragraphs in the Summary section to describe the Industrial Revolution 4.0 in Vietnam and the foundation of the need to conduct this data collection (see Line 57 - 73):

The goal of this dataset is not only to describe students' ICT cognitive and non-cognitive skills but also to encompass a thorough examination of the relationship between demographic, cognitive, behavioral, socio-cultural and contextual factors and digital competencies of school students. Utilizing both the frequentist and Bayesian approaches, analyses of these data would shed light on possible predictive factors and determinants of ICT proficiency as an essential ability of future global citizens.

This research is particularly crucial to developing economies which rely heavily on technology transfer to boost technological progress and sustain long term economic growth [1] Research has confirmed the underlying significance of human capital stock in secondary and tertiary levels of education, as absorption capacity, in facilitating technology transfer [1,2]. ICT skills readiness of school students is even more critical for low and middle-income economies in the era of Industry 4.0 with substantial challenges and opportunities. In Vietnam as a particular case study, researchers have pointed out the increased risks for the "middle-income trap" where investment will recede due to the rising labor cost and poor labor-saving technology [3]. Skills shortages are another problem facing the South-East Asian country with the highest percentage of wage workers at risk due to automation in the region [4]. As of 2016, low skilled workers still took up to over one-third of the total labor force in the country [5] despite a growing demand for IT workers by 47% per year [6]. Alongside the possible disruption in growth rates and concerns over the weak human capital, Vietnam's cybersecurity is still vulnerable with a four-fold increase of the number of cyberattacks and incidents within one year from 2015 to 2016 [7]. Vietnam only ranked 101 out of 193 countries in a global cybersecurity index in 2017 [8]. Recognizing this peril of falling far behind in the age of digitalization, the Vietnamese government has made an effort to improve skills education for youth, especially technical education [9]. In addition to enhancing the competitiveness of the labor force, digital competencies are also relevant to widespread concerns facing the global community such as cyberbullying, youth suicide, depression or behavioral disorders [10]. Findings from this dataset are, therefore, expected to have significant implications for the development and evaluation of management and capacity building policies in the emerging country.

+ While those consuming the manuscript and respective data set can figure out how the male/female data is coded (1 or 2), please consider labeling these values as male or female (instead of 1 and 2) in Figures 1 and 2 and in the respective text (see L94, L117).

We have changed the legends in Figure 1 and 2 from coded 1 and 2 to male and female for clarification.

+ Also, I’m assuming the 99 values in the data set are missing data? Please consider offering the codes in the data set so that the enumerated values are appropriately mapped. While redundant in some respects giving the manuscript discussion and findings, please simply make it clear (e.g., 1=male, 2=female, etc.).

We have provided the codebook and coding instruction files for clarification of the coded variables in the dataset.

We appreciate the hard work and time that you have spent on this manuscript. Your comments and suggestions have helped us improve the quality of our paper. We hope that the revised paper has met your requirements.

Please accept our sincere thanks for your great contributions to the improvement of higher studies on education and the overall advancement of sciences in the world.

Shall you have further comments, we look forward to hearing from you.

Best regards,

Round  2

Reviewer 2 Report

The authors have corrected and explained the merit and limitation of these dataset.

Thanks.