A Theoretical Framework for Analyzing Student Achievement in Software Education

Round 1

Reviewer 1 Report

Manuscript Number: 2055943

Article Type: Research Article

Full Title: Design of a Strategy Framework for Software Education

Objective: The present study aimed to explore students’ software intentions and construct a classification system for education intentions to establish a software education strategy.

The manuscript needs more work and effort in each section:

First of all, author(s) should proofread their text. There are some mistakes in the text. The writing and coherence of the text and the structuring of ideas need improvement.

Abstract should provide more details about the methodology (sample, data analysis, etc.).

Based on related literature, the authors proposed a software education strategy framework. Then they collected qualitative data to confirm the proposed framework. Software education strategy framework should be moved to Research Model or Theoretical Framework section.

Method section should mention sample and procedure. Further, interview questions should be provided in this section.

Since this is a qualitative study, themes, categories and codes resulted from statements should be provided in a Table.

Data analysis process should be detailed. Which software or technique was used to analyze qualitative data?

Descriptive Statistics (Mean, S.D., F) for each category and code should be reported.

Author(s) need to modify the conclusion by considering the above-mentioned points and modifying the results.

Author Response

Dear Reviewer   Thank you for the opportunity to revise our manuscript, Design of a Strategy Framework for Software Education. We appreciate the careful review and constructive suggestions. It is our belief that the manuscript is substantially improved after making the suggested edits. Based on your  guidance, we have tried our best to completely address your comments. Our replies (in RED) are  below.  The  revisions  are  colored  in  RED in our revised manuscript. We hope this version of our submission of the manuscript is now acceptable and fulfils the expectations of the reviewer.   Comments :   1. Comment :  Abstract should provide more details about the methodology (sample, data analysis, etc.).   Answer >>  We appreciate your thorough review and helpful suggestions to improve this paper. Thank you for these observations. We have rewritten the abstract to better differentiate among the objectives and edited so that the methods are reflected in the results and the data support the conclusions. Therefore, we have additionally described details of analysis procedure  in abstract.   An empirical experiment was carried out in a basic Python programming class in order to find variables in the framework. The framework was verified in terms of three aspects: utility, convenience, and elasticity.   2. Comment :  Based on related literature, the authors proposed a software education strategy framework. Then they collected qualitative data to confirm the proposed framework. Software education strategy framework should be moved to Research Model or Theoretical Framework section.-   Answer >>  We agree with the reviewer’s comment.  A process that statistically describes the strategic framework’s modeling process was added to Research Model. We presented the descriptive statistics of each variable in detail and added a description of the educational objectives of the research model. The educational objective was set in line with the new educational environment in the era of the Fourth Industrial Revolution, which helped to make this paper more persuasive. We appreciate your comment.   3. Comment :   Method section should mention sample and procedure. Further, interview questions should be provided in this section.   Answer >>   We agree with the reviewer’s comment. In Research Model, we presented the procedural characteristics of the research model. And, to present it more clearly, we analyzed the prior research statistically in Literature Review. For example, we verified the impact on dependent variables and each factor through the structural equation modeling analysis. We agree with the reviewer’s comment on the expert interview. Interview questions were added to the manuscript, and the characteristics and content of the questions were also added. All this helped to strengthen the legitimacy of this methodology. Thank you very much for your comment. We have supplemented the description of  the interview questions in result section.   These three questions are as follows. Question 1. a: Is this framework appropriate as a methodology for showing the tra-jectory of academic achievement? Can the trajectory be properly used in education? Question 1. b: This framework intends to show how students’ academic performance can change. What role does this framework serve to achieve this? What aspects need to be improved? Question 2. a: Can this framework be applied to various learning processes in the SW industry? Question 2. b: The SW industry has a range of the latest technologies and fields of work. What aspects of this framework need to be improved to be used in various SW fields? Question 3. a: Is it easy for professors and researchers to use this framework? Question 3. b: Does this framework have any characteristics that make it easy to ap-ply to learning strategies? What improvements are needed for professors to use this framework simple and easy?   4. Comment : : Since this is a qualitative study, themes, categories and codes resulted from statements should be provided in a Table.   Answer >>   Thank you so much for your comment.  We agree with and appreciate the reviewer's comment. The additional analysis of the reviewer’s comment is two-fold: The first is procedural stability to the 2*2 matrix methodology. This was supplemented by using statistical data and prior research. This has helped to make this paper’s arguments more solid and ultimately prove the academic value of this framework in detail. So, we are very grateful for the reviewer’s comment. The second is about introducing a more objective content analysis of FGI in the strategic framework. We thought about the information users can obtain after using the strategic framework for a certain period from the perspective of professors using it. Intensive individual interviews were conducted with a total of 9 users (professors, SW engineers, researchers) who are actually going to use this framework. The reliability of the result could vary depending on the interviewer’s ability to interview and analyze. And, although there were common questions, the following question could also be different depending on the respondent’s answer to the previous question. Therefore, it was necessary to be able to interpret all of the answers of the respondents with the same dimension. That’s why we prepared explanatory and simulation materials based on existing content analysis materials. This helped respondents to use this framework with a deep understanding of it.  We collected detailed information with emotional connections in a relaxed atmosphere. and even the grounds for their answers comments, and non-verbal reactions. The result of the interviews has been added in the table below.   And the information collected from the interviews, which is qualitative data, was refined through the content analysis technique for framework analysis. Next, in the process of sense-making, the information was classified by unit and then divided based on core consistencies and meanings to calculate the descriptive evaluation index. This process serves as an opportunity to analyze the intrinsic characteristics of the framework. Once again, we appreciate the reviewer's comment. Therefore, we have additionally described details of  content analysis procedure  and provide result data Table in result section.   At the beginning of the interview, we explained the direction of FGI and content analysis to professors and researchers from the user group, and the SW curriculum used in modeling the methodology to SW engineers. During the FGI process, we prepared for the interviewer to collect content analysis data after the interview was over. By analyzing all the phrases in the interview, we extracted 94 characteristics, 38 advantages, and 23 improvements. A pre-test was conducted on the data and exceptions were deleted. Then the data was divided by concept with duplicate and similar items in it being deleted. As a result, data was organized into 15 “advantage” units and 9 “improvement” units. These units were then divided into a total of 7 categories. We analyzed units in each category and conducted SPSS’ multiple response analysis for analytical factor values, deriving 4 “advantage” items and 3 “improvement” items in the strategic framework. These 7 categories received a high rating of 10% or higher. Exceptional cases of the methodology were identified by performing feedback on the 3 “improvement” units [42].   5. Comment : : Data analysis process should be detailed. Which software or technique was used to analyze qualitative data?   Answer >>  We agree with the reviewer’s comment. Adding this part has made the structure of the methodology clearer. We are very grateful for your advice. The authors followed this advice and proofread the method chapter to improve the manuscript. The derivation process for hedonic motivation and effort expectancy was described based on descriptive statistics, and the software used for the analysis and the analysis procedure was also explained in detail. The content analysis of the interview in the “Result” chapter also elaborates on the derivation process of the results. As a result, all the comments for improvement from FGI were reflected in the methodology.   The FGI interview on the framework was conducted with the in-depth interview method. There are two steps in this process: FGI, which is a preliminary step, and content analysis, which is a result analysis step. A group of users (professors, researchers, SW engineers) were invited for us to check the user experience of and improvements to the framework. The respondents in the interview were classified into different groups depending on their characteristics. The analysis procedure was detailed according to the content analysis definition of the prior research for framework analysis. The interview answers were dissected by sentence and pre-processing was conducted. Then the data is divided by unit and an evaluation index is extracted based on core consistencies. The analysis model for this content analysis is described in Figure 7. Step 1: Sentences are classified as “advantage” units and “improvement” units. Step 2: Perform a pre-test to find representative words. These words are integrated and classified depending on their similarity. Step 3:  Check the hierarchy of words. Evaluate and categorize the similarities of sub-concepts in the structure. Step 4:  Perform a statistical analysis through SPSS and then identify relations between objects.   6. Comment : Descriptive Statistics (Mean, S.D., F) for each category and code should be reported.   Answer >>  We appreciate the great comment from the reviewer. Based on the reviewer's comment, the theoretical background for this research model and the statistical explanation of the experimental results were added in Literature review. A description of the variables was added in Research method and the collected data was verified. The statistical characteristics of hedonic Motivation and effort expectancy were also explained. These variables statistically describe the derivation process of the SW education strategy framework and analyze it with the content analysis of interviewees in FGI. Detailed result units and categories were empirically derived.   We have supplemented the description in literature review and research model section. These statistical results are shown in Table 1 below. And we added table 1. These statistical results are shown in Table 2 below. And we added table 2. The descriptive statistics of HM2, EE3, and C3 are shown in Table 3, with the mean value being the highest in C3. And the box-whisker plot of two variables is described in Figure 3. For HM2, the quartile ranges of Grade “A” and Grade “B” were found to be the same. For EE3, the quartile range of Grade “A” is 4-6, less scattered than that of Grade “B”. For C3, it is found that the quartile range of Grade “A” is significantly different from and does not overlap with that of Grade “B”. However, outliers were found in Grade “A”. And we added Table 3 and Figure 3.   7. Comment : Author(s) need to modify the conclusion by considering the above-mentioned points and modifying the results.    Answer >>  We agree with the reviewer’s comment.  The conclusion of the current version is not based on empirical analysis results, leading to it being not fully organized. So, an empirical explanation was added to the conclusion reflecting the reviewer's comment. In addition, we added some content related to discussions to the conclusion, particularly the research methodology and the evaluation of the improvements in the FGI results. All this further strengthened the paper’s logic. We are very grateful for the reviewer’s comment. The prior research of this framework analyzed the learning intentions of Python programming class students. The relationship between learning factors was identified through regression analysis of data and logistic regression analysis. Then a decision tree analysis was applied to the results to look into the importance of the variables. Based on this data, we designed a matrix using hedonic motivation and performance expectancy to raise students' intentions for continuous learning. Additionally, the methodology was constructed by applying the confidence value identified in the multiple regression analysis as the third variable. We have supplemented the description in conclusion section.   The prior research of this framework analyzed the learning intentions of Python programming class students. The relationship between learning factors was identified through regression analysis of data and logistic regression analysis. Then a decision tree analysis was applied to the results to look into the importance of the variables. Based on this data, we designed a matrix using hedonic motivation and performance expectancy to raise students' intentions for continuous learning. Additionally, the methodology was constructed by applying the confidence value identified in the multiple regression analysis as the third variable. In the FGI results, “promptness” accounted for the highest proportion of all categories. The education strategy framework has the characteristic of being a tool that can be directly applied to the educational field. In other words, the first goal is for professors to quickly determine students' learning performance. The second biggest part following that of promptness is “elasticity.” This indicates what characterizes the software field. While the software industry has a variety of structures, some similarities go through all development processes. Plus, there are similarities in human resources, equipment, tools, languages, etc. as well. This makes the software industry an industry with high mobility and similarity compared to other industry fields. In this respect, elasticity is one of the major traits that uphold this framework. In terms of the intuitive nature of the framework and the empirical aspect of education, the results of units of reliability (trajectory reliability, universality, storage of results, reflection of information) require a rapid application. To meet this condition, it is necessary to continue to supplement the framework in a long-term and detailed manner. Applying exceptional variables to the items marked as negative in Table 4 can be considered later for the framework proposed in this study. For example, we may be able to judge future achievement by reflecting certain values’ biases in the learning performance trajectory. By applying weighting elements to the framework this way, it will be possible to update the framework in a more sophisticated manner.   In the era of digital transformation, the potential that digital technology holds in the labor market is huge. With the spread of digital technology, the degree of automation is constantly increasing in major fields in the SW industry. Work productivity is increasing, and job quality is improving. From the beginning of 1980 to the second half of the 2010s, wages grew rapidly along with technological maturity in the information industry. The convenience provided by digital technology plays a big role in this part.  This process is directly related to changes in digital and production processes and is evolving into the form of a smart factory. In this regard, SW education provides students with job stability in the labor market. This framework will help students locate where they are at in the SW education stage and serve as a guide in an educational curriculum to become an ad-vanced SW engineer.    

Author Response File: Author Response.pdf

Reviewer 2 Report

Article suitable for the journal, with an interesting scientific contribution.

1.    It is recommended to adjust the title to make the contribution clear.

2.    In the abstract add the applied research method. 3.    It is convenient to specify from the title the teaching time of what type of software. 4.    Expand the review of the literature according to the type of software teaching, since it is very general as it is presented. 5.    Add some descriptive statistics that help to interpret educational interventions. 6.    Add results discussion section.

 

Author Response

Reviewer #2   Dear Reviewer   Thank you for the opportunity to revise our manuscript, Design of a Strategy Framework for Software Education. We appreciate the careful review and constructive suggestions. It is our belief that the manuscript is substantially improved after making the suggested edits. Based on your  guidance, we have tried our best to completely address your comments. Our replies (in RED) are  below.  The  revisions  are  colored  in  RED in our revised manuscript. We hope this version of our submission of the manuscript is now acceptable and fulfils the expectations of the reviewer.   Comments :   1.  Comment :  It is recommended to adjust the title to make the contribution clear. Title  : A Theoretical Framework for Analyzing Student Achievement in  SW Education Answer >>  We appreciate your thorough review and helpful suggestions to improve this paper.  We agree with the reviewer’s comment. The title was not enough to indicate the empirical side of the results. So the authors contemplated the academic contribution of this framework. The goal of this study is to easily apply the framework methodology to SW education. This methodology has strengths in empirical analysis of student achievement. Therefore, the authors decided to reflect the reviewer’s opinion and change the title. Along with changing the title, it was necessary to add a logical explanation of student achievement and parameters in the paper as well. We described this process in Literature review in detail.   We have supplemented the description in research model section.   The first is the descriptive statistics based on the description of the variables used in Yoo, et al. (2020) [23] and collected data. The measurements of hedonic motivation, effort expectancy and confidence are visualized so that it is possible to check the chances of applying them to the SW education strategy framework. The questions for the variables are as follows and respondents were asked to give their answers on a scale of 1 to 7.   HM2: I find Python programming interesting. HM1: I find Python programming to be enjoyable. EE3: Python programming is easy for me to use. C3: I feel that if I work hard in this class, I can succeed. Among them, HM1 and HM2 are related to hedonic motivation, so it is necessary to select variable that reflects it. HM2, EE3, and C3 are corresponding to score, so HM2 is used.     2. Comment : In the abstract add the applied research method. Answer >>  We appreciate your thorough review and helpful suggestions to improve this paper. Thank you for these observations. We have rewritten the abstract to better differentiate among the objectives and edited so that the methods are reflected in the results and the data support the conclusions.   3. Comment : It is convenient to specify from the title the teaching time of what type of software.   Answer >>   We agree with the reviewer’s comment. The types of software used by the authors for empirical analysis in this study are Python programming language and basic computing technology (algorithms, grammar, development environment). Python language is one of the most popular elements in the SW curriculum in Korean universities, along with C language. Meanwhile, basic computing technology is about the overall and basic knowledge of SW technology. So it was not easy to condense all this into a short title. Still, the authors embraced the review’s comment and added more details in Introduction and Literature review although failing to contain them in the title.   The lecture used in the empirical study of this prior research deals with Python programming and basic computing skills. The Python programming chapter covers basic grammar, development environments, and simple projects. Computing skills deal with the overall and basic knowledge of SW technology.   We have supplemented the description in introduction and research model section.   This study is organized as follows. Chapter 2 reviews the literature of this study. Chapter 3 describes the model of strategy framework. Chapter 4 describes the results with expert interview. And Chapter 5 deals with conclusions, and future research.   To propose a research model based on the previous related studies, this study will proceed in the following way: First, check the descriptive statistics of the factors derived in Chapter 2, which are effort expectancy (EE3), hedonic motivation (HM2), and confidence (C3). Second, set the educational objective that meets the needs of the new environment in the Fourth Industrial Revolution. Third, suggest an SW education strategy framework based on the educational objective.   . The lecture used in the empirical study of this prior research deals with Python pro-gramming and basic computing skills. The Python programming chapter covers basic grammar, development environments, and simple projects. Computing skills deal with the overall and basic knowledge of SW technology.   4.  Comment :  Expand the review of the literature according to the type of software teaching, since it is very general as it is presented.   Answer >>   We are very grateful for the great advice from the reviewer. The authors described prior research directly related to this study in Literature Review. However, there was a lack of logical explanation in terms of strategic methodology. Following the reviewer’s advice, we added a variety of prior research, including various studies on educational attempts for hedonic motivation and effort expectancy. This helped to elaborate on the procedures of methodology and strengthened the logic of methodology. We really appreciate your comment.   We have supplemented the description in literature review section.   In particular, there have been several attempts to induce hedonic motivation among students in various studies. For example, Boytchev and Boytcheva (2020) improved students’ scores and boosted their motivation by applying gamified evaluation to their classes designed to improve computer graphics skills [21]. Also, Manzano-León, et al. (2021) utilized online escape rooms as part of their educational strategy to promote motivation among students in the Department of Education and Social Psychology [22]. It turned out that using gamification elements was helpful in encouraging students to debate and participate. For these reasons, hedonic motivation will be used as the first factor in this study.   Therefore, effort expectancy will be used as the second factor in this study.   Lastly, it is confidence that motivates learners and suggests strategies that help to implement their plan [24]. Confidence, one of the most important motivational factors among learners, is known to be the major factor that determines math competency [25] and computer skills [26]. Additionally, Yoo, et al. (2022)’s multi-regression analysis results proved that confidence has a positive impact on grades [23]. Although not derived from the decision tree analysis, this study will consider confidence the third factor.   5.  Comment :  Add some descriptive statistics that help to interpret educational interventions.  Answer >>  We appreciate the great comment from the reviewer. Based on the reviewer's comment, the theoretical background for this research model and the statistical explanation of the experimental results were added in Literature review. A description of the variables was added in Research method and the collected data was verified. The statistical characteristics of hedonic Motivation and effort expectancy were also explained. These variables statistically describe the derivation process of the SW education strategy framework and analyze it with the content analysis of interviewees in FGI. Detailed result units and categories were empirically derived.   We have supplemented the description in literature review and research model section.   These statistical results are shown in Table 1 below. And we added table 1. These statistical results are shown in Table 2 below. And we added table 2. The descriptive statistics of HM2, EE3, and C3 are shown in Table 3, with the mean value being the highest in C3. And the box-whisker plot of two variables is described in Figure 3. For HM2, the quartile ranges of Grade “A” and Grade “B” were found to be the same. For EE3, the quartile range of Grade “A” is 4-6, less scattered than that of Grade “B”. For C3, it is found that the quartile range of Grade “A” is significantly different from and does not overlap with that of Grade “B”. However, outliers were found in Grade “A”. And we added Table 3 and Figure 3.   This study uses grades separated by 40:60 as categories need to be used to use the box-whisker plot provided by SPSS v. 27 (IBM Korea, Seoul and Korea).   6. Comment :   Add results discussion section. Answer >>  We agree with the reviewer’s comment.  The conclusion of the current version is not based on empirical analysis results, leading to it being not fully organized. So, an empirical explanation was added to the conclusion reflecting the reviewer's comment. In addition, we added some content related to discussions to the conclusion, particularly the research methodology and the evaluation of the improvements in the FGI results. All this further strengthened the paper’s logic. We are very grateful for the reviewer’s comment. The prior research of this framework analyzed the learning intentions of Python programming class students. The relationship between learning factors was identified through regression analysis of data and logistic regression analysis. Then a decision tree analysis was applied to the results to look into the importance of the variables. Based on this data, we designed a matrix using hedonic motivation and performance expectancy to raise students' intentions for continuous learning. Additionally, the methodology was constructed by applying the confidence value identified in the multiple regression analysis as the third variable. We have supplemented the description in conclusion section.   The prior research of this framework analyzed the learning intentions of Python programming class students. The relationship between learning factors was identified through regression analysis of data and logistic regression analysis. Then a decision tree analysis was applied to the results to look into the importance of the variables. Based on this data, we designed a matrix using hedonic motivation and performance expectancy to raise students' intentions for continuous learning. Additionally, the methodology was constructed by applying the confidence value identified in the multiple regression analysis as the third variable. In the FGI results, “promptness” accounted for the highest proportion of all categories. The education strategy framework has the characteristic of being a tool that can be directly applied to the educational field. In other words, the first goal is for professors to quickly determine students' learning performance. The second biggest part following that of promptness is “elasticity.” This indicates what characterizes the software field. While the software industry has a variety of structures, some similarities go through all development processes. Plus, there are similarities in human resources, equipment, tools, languages, etc. as well. This makes the software industry an industry with high mobility and similarity compared to other industry fields. In this respect, elasticity is one of the major traits that uphold this framework. In terms of the intuitive nature of the framework and the empirical aspect of education, the results of units of reliability (trajectory reliability, universality, storage of results, reflection of information) require a rapid application. To meet this condition, it is necessary to continue to supplement the framework in a long-term and detailed manner. Applying exceptional variables to the items marked as negative in Table 4 can be considered later for the framework proposed in this study. For example, we may be able to judge future achievement by reflecting certain values’ biases in the learning performance trajectory. By applying weighting elements to the framework this way, it will be possible to update the framework in a more sophisticated manner.   In the era of digital transformation, the potential that digital technology holds in the labor market is huge. With the spread of digital technology, the degree of automation is constantly increasing in major fields in the SW industry. Work productivity is increasing, and job quality is improving. From the beginning of 1980 to the second half of the 2010s, wages grew rapidly along with technological maturity in the information industry. The convenience provided by digital technology plays a big role in this part.  This process is directly related to changes in digital and production processes and is evolving into the form of a smart factory. In this regard, SW education provides students with job stability in the labor market. This framework will help students locate where they are at in the SW education stage and serve as a guide in an educational curriculum to become an ad-vanced SW engineer.  

Author Response File: Author Response.pdf

Reviewer 3 Report

This study strategically analyzed student tendencies to propose a framework. To provide customized education for nonsoftware major students effectively, this study focused on developing systematic support from teachers by analyzing the distribution of the students.

Advice and wishes:

* in the study, it is desirable to add a review of modern research on the problem
* when choosing research methods, it would be nice to add a description of the selection algorithm and justification for its use
* in the article, to recommend the study of programming languages, it would be nice to take into account the current rating and the requirements of the labor market

Author Response

Reviewer #3   Dear Reviewer   Thank you for the opportunity to revise our manuscript, Design of a Strategy Framework for Software Education. We appreciate the careful review and constructive suggestions. It is our belief that the manuscript is substantially improved after making the suggested edits. Based on your  guidance, we have tried our best to completely address your comments. Our replies (in RED) are  below.  The  revisions  are  colored  in  RED in our revised manuscript. We hope this version of our submission of the manuscript is now acceptable and fulfils the expectations of the reviewer.   Comments :   1. Comment : in the study, it is desirable to add a review of modern research on the problem   Answer >>   We are very grateful for the great advice from the reviewer. The authors described prior research directly related to this study in Literature Review. However, there was a lack of logical explanation in terms of strategic methodology. Following the reviewer’s advice, we added a variety of prior research, including various studies on educational attempts for hedonic motivation and effort expectancy. This helped to elaborate on the procedures of methodology and strengthened the logic of methodology. We really appreciate your comment.   We have supplemented the description in literature review section.   In particular, there have been several attempts to induce hedonic motivation among students in various studies. For example, Boytchev and Boytcheva (2020) improved students’ scores and boosted their motivation by applying gamified evaluation to their classes designed to improve computer graphics skills [21]. Also, Manzano-León, et al. (2021) utilized online escape rooms as part of their educational strategy to promote motivation among students in the Department of Education and Social Psychology [22]. It turned out that using gamification elements was helpful in encouraging students to debate and participate. For these reasons, hedonic motivation will be used as the first factor in this study.   Therefore, effort expectancy will be used as the second factor in this study.   Lastly, it is confidence that motivates learners and suggests strategies that help to implement their plan [24]. Confidence, one of the most important motivational factors among learners, is known to be the major factor that determines math competency [25] and computer skills [26]. Additionally, Yoo, et al. (2022)’s multi-regression analysis results proved that confidence has a positive impact on grades [23]. Although not derived from the decision tree analysis, this study will consider confidence the third factor.     2. Comment : when choosing research methods, it would be nice to add a description of the selection algorithm and justification for its use.   Answer >>   We appreciate your thorough review and helpful suggestions to improve this paper. We agree with the reviewer’s comment. In Research Model, we presented the procedural characteristics of the research model. And, to present it more clearly, we analyzed the prior research statistically in Literature Review. For example, we verified the impact on dependent variables and each factor through the structural equation modeling analysis. We agree with the reviewer’s comment on the expert interview. Interview questions were added to the manuscript, and the characteristics and content of the questions were also added. All this helped to strengthen the legitimacy of this methodology. Thank you very much for your comment.   We have supplemented the description in introduction and research model section.   This study is organized as follows. Chapter 2 reviews the literature of this study. Chapter 3 describes the model of strategy framework. Chapter 4 describes the results with expert interview. And Chapter 5 deals with conclusions, and future research.   To propose a research model based on the previous related studies, this study will proceed in the following way: First, check the descriptive statistics of the factors derived in Chapter 2, which are effort expectancy (EE3), hedonic motivation (HM2), and confidence (C3). Second, set the educational objective that meets the needs of the new environment in the Fourth Industrial Revolution. Third, suggest an SW education strategy framework based on the educational objective.   3.  Comment : in the article, to recommend the study of programming languages, it would be nice to take into account the current rating and the requirements of the labor market   Answer >>  Thank you so much for your comment.   We agree with the reviewer’s comment. The authors got to understand the labor market of the SW industry while analyzing prior research, especially focusing on the potential of digital technology, As the reviewer commented, digital technology is evolving into ICT, Internet, smartphones, AI, IoT, big data, etc. and spreading into the value chains of each industry, commercial transactions and contracts, human labor, and everyday life. Even at this moment, new technologies are being created, spreading, and evolving in the digital technology field. We brought these considerations into the conclusion and discussions, which added details to the procedures of the methodology and strengthened the methodology’s logic. We are very grateful.    We have supplemented the description in conclusion section.   In the era of digital transformation, the potential that digital technology holds in the labor market is huge. With the spread of digital technology, the degree of automation is constantly increasing in major fields in the SW industry. Work productivity is increasing, and job quality is improving. From the beginning of 1980 to the second half of the 2010s, wages grew rapidly along with technological maturity in the information industry. The convenience provided by digital technology plays a big role in this part.  This process is directly related to changes in digital and production processes and is evolving into the form of a smart factory. In this regard, SW education provides students with job stability in the labor market. This framework will help students locate where they are at in the SW education stage and serve as a guide in an educational curriculum to become an advanced SW engineer.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

In the literature review, Table 1 shows the result of multiple regression analysis obtained from the study of [17]. Similarly, Table 2. shows the result of structural equation modeling analysis from the source [19]. Table 3 show the result of descriptive statistics from the source [23]. Since these results are not belong to the current study, the results should not be presented in the tables but can be explained in the text. This would be more appropriate for the academic standards.

 

The following studies may contribution to the current study; “The role of self-efficacy and perceived enjoyment in predicting computer engineering students’ continuous use intention of Scratch” “The role of self-efficacy in predicting use of distance education tools and learning management systems” and “predicting adoption of visual programming languages: An extension of the technology acceptance model.” Hedonic motivation can be related to the self-efficacy and perceived enjoyment, use findings of these works to enhance discussion and implications of the findings.

 

Author Response

Reviewer #1   Dear Reviewer   Thank you for your additional comments related to the structure of our paper. Based on your guidance, we have tried our best to completely address your comments. The  revisions  are  colored  in  RED in our revised manuscript. We hope this 2nd version of our submission of the manuscript is now acceptable and fulfils the expectations of the reviewer.   Comments :   1. Comment :  In the literature review, Table 1 shows the result of multiple regression analysis obtained from the study of [17]. Similarly, Table 2. shows the result of structural equation modeling analysis from the source [19]. Table 3 show the result of descriptive statistics from the source [23]. Since these results are not belong to the current study, the results should not be presented in the tables but can be explained in the text. This would be more appropriate for the academic standards.   Answer >>  We appreciate your thorough review and helpful suggestions to improve this paper.  The reviewer's comment helped us realize our mistake. We summarized three years of previous research to develop a strategic framework for SW education. This has led us to develop an integrated methodology, which is the academic achievement of this study. This explains why the authors explained their previous research in detail in Literature Review.  However, following the reviewer's comment that this type of description does not meet the academic standards, the previous research was described not in tables but in the text format. While the original version mainly dealt with the flow of the research methodology, the tables were deleted and figures were added to the original version in the revised one. This way of description helped to strengthen the legitimacy of the methodology. Thank you.     Following the reviewer's comment, we have amended the existing descriptions of references [17] and [19] in the Literature Review section as follows:   As a result of performing multiple regression analysis using the unified theory of ac-ceptance and use of technology by Venkatesh et al. [18], they found that performance ex-pectancy (t=6.624, p<0.001), effort expectancy (t=3.232, p<0.01), and social influence (t=5.664, p<0.001) had a statistically significant effect on Python programming intention. The analysis showed that hedonic motivation (t=4.42, p<0.001) had the greatest influence on the dependent variable, followed by performance expectancy (t=5.496, p<0.001), social influence (t=4.517, p<0.001), and facilitating conditions (t=2.982, p<0.01). The path coeffi-cient of effort expectancy (t=0.774, p>0.05) had no statistically significant influence.   The following sentence has been added to the descriptions of reference [23]:   The analysis result shows that the average of C3 (M=5.594, S.D=1.40628) is the highest, followed by HM2 (M=4.58, S.D=1.649) and EE3 (M=4.05, S.D=1.713).   2. Comment :  The following studies may contribution to the current study; “The role of self-efficacy and perceived enjoyment in predicting computer engineering students’ continuous use intention of Scratch” “The role of self-efficacy in predicting use of distance education tools and learning management systems” and “predicting adoption of visual programming languages: An extension of the technology acceptance model.” Hedonic motivation can be related to the self-efficacy and perceived enjoyment, use findings of these works to enhance discussion and implications of the findings.   Answer >>  We agree with the reviewer’s comment.  We read the three papers introduced by the reviewer and checked their relevance to our paper. In particular, the reviewer's comment on hedonic motivation greatly helped to improve the quality of this paper. The previous research was used to strengthen the description in the discussion. The authors are studying a methodology to quantitatively evaluate the social impact of SW education as a follow-up study. We believe the papers introduced by the reviewer will be greatly helpful to the follow-up study. This paper includes an elaborate methodology related to education, which have great relations with the authors' research (education, social impact, and technologies related to the education). We appreciate your comment.   Following the reviewer's comment we included in Conclusion and Discussion the following sentence:   Meanwhile, the UTAUT model used in this study was made based on 8 related theories, including the technology acceptance model (TAM). Effort expectancy is related to perceived ease of use in TAM [47]. According to research by Arpaci (2017), self-efficacy, which refers to a judgment of students' ability to use a system, statistically affects perceived ease of use. In other words, it is expected that raising self-efficacy by differentiating the difficulty between beginners and challenges will increase effort expectancy. And hedonic motivation is related to perceived enjoyment [49]. Arpaci and Durdu (2019) and Arpaci (2021) applied the Scratch language, which was taught to computer science students, to TAM and found that perceived enjoyment have a statistic impact on attitude, and the parameter does on continuous use intention. [50,51]. In particular, perceived enjoyment has the biggest impact on attitude, which makes it deeply related to existing research [19]. Therefore, it is necessary to look into the applicability of TAM in future research.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors applied the recommendations appropriately.

Author Response

Dear Reviewer    Comments   1. Comment :   The authors applied the recommendations appropriately.   Answer >>   Thanks for the reviewer's positive comment.

Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

The authors have addressed all of my concerns.