Factor Analysis of Computational Thinking for Software

Factor Analysis of ComputationalThinking for Software Education Based

on Problem-Solving Learning

Youngseok Lee1 and Jungwon Cho∗2

1KNU College of Liberal Arts and Sciences,Kangnam University, 40 Gangnam-ro Giheung-gu,

Yongin-si, Gyeonggi-do, 16979,South Korea

[email protected]∗2Department of Computer Education,

Jeju National University, 102 Jejudaehakno,Jeju-si, Jeju-do, 63243 South Korea

Corresponding Author:[email protected]

Abstract

Background/Objectives: In a software-oriented soci-ety, changes are taking place in many fields as well as ininterdisciplinary convergence. For this purpose, we attemptto analyze the factors influencing computational thinkingability by conducting software education based on problem-solving learning that can enhance students’ interest in com-puting.

Methods/Statistical Analysis: We conducted soft-ware according to the procedure and method of problem-solving learning. We found that computational thinkingimproved through computational thinking and performance.Factor analysis and structural equation modeling were con-ducted to clarify the factors.

Findings: We analyzed students’ satisfaction with theresults of problem-solving based software education as well

International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 4953-4967ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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as the relevance of the results. Based on this, we triedto determine the improvements in computational thinkingability and clarify the relevant factors. Although we did notclarify the relation with the grades, it was found that fourfactors (pattern, abstraction, automation, and algorithm)influenced computational thinking ability.

Improvements/Applications: If we specify the con-tent and method of problem-solving learning according tothe results of the analysis of concrete factors influencingcomputational ability, we will be able to make software ed-ucation interesting.

Key Words : Problem-Solving Learning, Software Ed-ucation, Computational Thinking, Educational Program-ming Language, Factor Analysis, Structural Regression Model.

1 Introduction

Modern society is software-centric, with changes taking place inmany fields as well as convergence between disciplines. Many uni-versities have implemented computer literacy courses based on soft-ware education for all students regardless of their field of study,including the ability to design and develop software [1]. The aimsor focus of software education is to develop ideas by solving prob-lems through computational thinking and to cultivate creativity instudents through the process of creating their own results [2].

Computational thinking is the recognition of problems accord-ing to the basic concepts and principles of computer science, itimproves students’ ability to solve problems, which is a basic skillfor their future endeavors/activities [3, 4]. Through software, stu-dents can shape their ideas by applying computing technology intheir respective fields and develop the ability to communicate withexperts in various fields. This kind of computational thinking canbe developed and improved through software education based oneducational programming language [5].

However, in reality, students who are not computer science ma-jors do not believe in the necessity of software education basedon educational programming language. Therefore, it is to improvecomputational thinking and problem-solving abilities while attract-

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ing the interest of students [3, 6]. In order to develop problem-solving skills, students are required to present problems based onreal life, identify and investigate these problems, and study theseproblems as a series of processes in which problem-solving learningcan be implemented [6].

Therefore, in this paper, we apply software education based onproblem-solving learning that can enhance computational thinkingwhile encouraging college students’ interest in software education.We also analyze the factors that influence computational thinkingalong with students’ satisfaction.

We construct a structural regression model based on the resultsof factor analysis and analyze the impact of software education onstudents’ achievements after completing their education.

2 Materials and Methods

2.1 Software Education

Software education involves learning a programming language thatcan communicate with a computer and then running the programand checking the results according to the procedures and methodsdeveloped. At this time, we can distinguish between professionalprogramming education to train software developers and univer-sal programming education to improve computational thinking andproblem-solving abilities [7].

In this paper, we focus on universal programming educationto train talented people in computational thinking and problem-solving in preparation for the Fourth Industrial Revolution [7].Programming finds ways to solve problems and then applies thesemethods in the course of solving a given problem, and graduallyrepeats and combines processes to complete the work [8].

From this point of view, various skills such as information pro-cessing, procedural thinking, problem-solving, logical thinking, andreasoning are improved through programming education. Sincenon-computer majors have difficulty learning about programming,concepts have to presented from a totally different perspective. Inorder to reduce this difficulty, it is necessary to start with a prob-lem that students can easily solve, and then to construct it so thatstudents can solve various difficult situations [8].


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2.2 Problem-Solving Learning

Problem-solving learning is used in a similar way as problem-basedlearning [9]. In this learning method, learning is achieved througha series of processes in which problems are presented to the stu-dent, who then searches for solutions to the problems and developssolutions through individual or collaborative learning [10].

Problem-solving learning can improve the student’s ability tosolve problems by understanding problems, developing problem-solving plans, and practicing, revising, and re-implementing prac-tical methods [6, 11]. Based on the instructional stage of problem-solving learning in previous research, we propose the following fea-tures of problems that are required for the software education pro-posed in this paper [12, 13].

- Problems that involve real-life situations

- Problems with more than one solution

- Motivating and challenging issues

- Problems with expansion and decomposition that require ab-straction and automation

In general, the programming process is similar to the problem-solving process, thus this paper analyzes the research on problem-solving learning in various fields and applies it to software education[13, 14].

[Figure 1] shows the result of summarizing the learning proce-dure in order to perform problem-solving learning [9]. After in-troducing the basics of programming, the problem is presented tothe student along with the background of the problem. Studentsidentify the problems that need to solve, what they need to solve,and determine whether the problems can be solved. If they do notunderstand the problem, they can review the basic elements of theproblem. If they can check and judge the problem, then they canlearn how to solve it or learn additional information [10].

If the student can solve the problem, then the solution is summa-rized and presented. If the student cannot solve the problem, thenthe problem should be extracted and re-examined [8, 10]. Each


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problem is arranged according to class objectives, problem con-tents, and class time. At the beginning of the class, problems arepresented simply and easily, and students learn to solve complexproblems gradually. Repeated learning was also performed [13].The basic lecture plan is similar to the programming education fornon-computer specialists proposed in [14], but the course contentsare modified so that problem-solving techniques can be applied.

Figure 1: Procedures For Problem-Solving Learning


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3 Results and Discussion

3.1 Subject Of Experimental Study

From 2016∼2017, Python language-based software education wasprovided to the first-year students of K-University. Information onthe students is listed in [Table 1], [Table 2].

The faculties are divided into six departments, and the numberof students per faculty is shown in [Table 1]. Most of them havetaught computer specialists, and computer specialists have taught68 students in two departments. Male students outnumbered femalestudents, but no significant difference was found between homosex-uals.


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3.2 Satisfaction Factor Analysis of Problem-Solving Learning

[Table 3] shows the results of the satisfaction factor analysis ofproblem-solving learning.

The results of the factor analysis showed the contents related toproblem-solving learning, and students responses to the character-istics of each item such as attendance and grades.


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The results of the survey are shown in [Table 4]. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s test scores reached as high as.872 and the significance level was .000, thus confirming the valid-ity of the item. As a result, it seems that students showed no spe-cific tendencies or situations. Therefore, we attempted to analyzestudents’ computational thinking ability through problem-solvinglearning.

3.3 Factors Analysis of Computational Think-ing

In order to evaluate the appropriateness of the proposed softwareeducation model, software education was used and computationalthinking ability suggested by the Korea Institute of EducationalScientific Information was employed as a testing tool to measurestudents’ computational thinking ability [14]. [Table 5] shows theresults of the factor analysis of the validity of the computationalthinking ability test. The KMO and Bartlett’s test score was .687and the significance level was .000.

[Table 6] shows the results of the factor analysis of the com-putational thinking ability test in order to determine which of theeight factors influenced computational thinking ability.


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The five variables include pattern, abstraction, algorithm, au-tomation, and reasoning, and the computational thinking totalscore. However, the results of factor analysis showed that the rea-soning value is low, thus the factors were extracted from the domainof pattern and algorithm, and four factors were analyzed, namelypattern, abstraction, algorithm, and automation. The results of theexploratory factor analysis of the four factors are shown in [Figure2].


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Figure 2: Result Of Unstandardized Estimates

The conformity criterion of the measurement model was An-derson ’s model conformity report [15]. [Table 7] shows the re-sults of analysis using the root mean square residual (RMR) andgoodness-of-fit index (GFI). The conformity of the structural re-gression model was .004 for RMR and .982 for GFI.


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In this paper, the RMR is less than .05 and GFI is .90 or more,because the number of cases is greater than 200 and the numberof measurement variables is 12. Therefore, it can be assumed thatthe model satisfies the criteria of conformity.

Figure 3: Final Structure Regression Model (Unstandardized)


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Based on this, the results of model estimation and statisticalsignificance of the structural regression model are shown in [Figure3] and [Table 8].

As shown in [Table 8], the variables of algorithm, pattern, ab-straction, automation have a positive influence. It can be under-stood that the factors extracted influence computational thinkingability. However, the factors influencing computational ability donot affect the grades; thus, more detailed research is needed.

4 Conclusion

Computational thinking is considered one of the most importantskills that students need regardless of their major, in preparationfor the coming Fourth Industrial Revolution. To this end, softwareeducation as a universal education should be structured so as tomake the experience of problem-solving using computing technol-ogy interesting. However, many universities still fail to go beyondthe limits of current programming language education.

In this paper, we designed software education as a problem-solving learning which is the most used educational programminglanguage in colleges, and learn by using the solution according tothe presented problem situation. As a result, it contributed toimprovement of students’ computational thinking ability, analyzedthe concrete factors; extracted the algorithm, pattern, abstract, andautomation factors; and analyzed the influence of these factors.

In the future, we will analyze the correlation between actualproblem-solving process, attendance, and academic achievement,and then study ways to improve computational thinking and problem-


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solving abilities based on this research. We plan to conduct softwareeducation with different topics and then verify their effectiveness.

References

[1] Rosales, V. A., Queiruga, C., Tzancoff, C. B., Kimura, I. M.,& Bartneche, M. B. (2017, September). Programming com-petitions in high school classrooms: RITA en RED. In Com-puter Conference (CLEI), 2017 XLIII Latin American (pp. 1-8). IEEE.

[2] G. Chen, J. Shen, L. Barth-Cohen, S. Jiang, X. Huang, &M. Eltouhky. (2017). Assessing Elementary Students’ Compu-tational Thinking in Everyday Reasoning and Robotics Pro-gramming. Computer and Education, 109, 162-175.

[3] Kemp, P. (2014). Computing in the nationalcurriculum: A guide for secondary teach-ers. Computing at School. Retrieved fromhttp://www.computingatschool.org.uk/data/uploads/cas sec-ondary.pdf.

[4] Grover, S., & Pea, R. (2013). Computational thinking in K12:A review of the state of the field. Educational Researcher,42(1), 38-43.

[5] Wing, J. M. (2006). Computational thinking. Communicationsof the ACM, 49(3), 33-35.

[6] Czerkawski, B. C., & Lyman, E. W. (2015). Exploring issuesabout computational thinking in higher education. TechTrends,59(2), 57-65.

[7] Harvard University. (2015). CS50 syllabus, Retrieved fromhttp://cdn.cs50.net/2015/fall/lectures/0/w/syllabus/cs50/cs-50.html.

[8] Psycharis, S., Kalovrektis, K., Sakellaridi, E., Korres, K., &Mastorodimos, D. (2018). Unfolding the Curriculum: Phys-ical Computing, Computational Thinking and Computational


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Experiment in STEMs Transdisciplinary Approach. EuropeanJournal of Engineering Research and Science, (CIE), 19-24.

[9] Almeida, R., & Pessoa, T. (2017 November). Scratch softwarein higher education: Pedagogical experience in educational sci-ence. In Computers in Education (SIIE), 2017 InternationalSymposium on (pp. 1-5). IEEE.

[10] Higgins, C., Mtenzi, F., Oleary, C., Hanratty, O., & McAvinia,C. (2017, July). A Software Development Process for FreshmanUndergraduate Students. In IFIP World Conference on Com-puters in Education (pp. 599-608). Springer, Cham.

[11] Kim, J. A., & Ko, D. Y. (2017). Survey of On-Line & BlockProgramming Language-Scratch: On Perspective of Educa-tional Achievements. In Advances in Computer Science andUbiquitous Computing (pp. 35-40). Springer, Singapore.

[12] Jiang, S., & Wong, G. K. (2017, December). Assessing primaryschool students’ intrinsic motivation of computational think-ing. In Teaching, Assessment, and Learning for Engineering(TALE), 2017 IEEE 6th International Conference on (pp. 469-474). IEEE.

[13] Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2018). Developing theComputer Programming Self-Efficacy Scale for Computer Lit-eracy Education. Journal of Educational Computing Research,0735633117746747.

[14] Youngseok Lee and Jungwon Cho. (2017). The Influ-ence of Python Programming Education for RaisingComputational Thinking. International Journal of u-and e- Service, Science and Technology, 10(8), 59-72.http://dx.doi.org/10.14257/ijunesst.2017.10.8.06.

[15] Anderson, J. C., & Gerbing, D. W. (1988). Structural equationmodeling in practice: A review and recommended two-step ap-proach. Psychological bulletin, 103(3), 411.


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Factor Analysis of Computational Thinking for Software