Diogene-CT: tools and methodologies for teaching and learning coding
Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students...
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| Vydáno v: | International Journal of Educational Technology in Higher Education Ročník 18; číslo 1; s. 1 - 26 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cham
Springer International Publishing
05.03.2021
BioMed Central, Ltd Springer Nature B.V SpringerOpen |
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| ISSN: | 2365-9440, 2365-9440 |
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| Abstract | Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME (“Code Animation by Evolved Metaphors”) that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments. |
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| AbstractList | Abstract Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME (“Code Animation by Evolved Metaphors”) that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments. Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME ("Code Animation by Evolved Metaphors") that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments. |
| ArticleNumber | 12 |
| Audience | Higher Education Postsecondary Education Secondary Education |
| Author | Mecca, Giansalvatore Veltri, Enzo Santoro, Donatello Sileno, Nazzareno |
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| Cites_doi | 10.1145/1089733.1089734 10.1109/EIT.2018.8500194 10.1145/1118178.1118215 10.1145/1345375.1345441 10.1145/2490822 10.1145/1539024.1508989 10.1006/jvlc.2002.0237 10.1076/csed.13.4.249.17496 10.1145/989863.989928 10.1145/1538234.1538236 10.1145/3077618 10.1016/j.compedu.2013.10.020 10.7551/mitpress/1972.001.0001 10.1145/1227504.1227481 |
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| Copyright | The Author(s) 2021 The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References | Dehnadi, S. (2006). Testing programming aptitude. In PPIG, (p. 9). WingJMComputational thinkingCommunications of the ACM2006493333510.1145/1118178.1118215 da Silva, T.R., Medeiros, T., & da Silva Aranha, E.H. (2015). The use of games on the teaching of programming: A systematic review. In CIbSE (p. 474) Siekmann, G., & Korbel, P. (2016). Defining stem skills: Review and synthesis of the literature. In NCVER. Commonwealth of Australia, AU. Jawad, H.M., de Laski-Smith, D., & Tout, S. (2018). The code genie programming environment. In 2018 IEEE International Conference on Electro/Information Technology (EIT) (pp. 0163–0168). https://doi.org/10.1109/EIT.2018.8500194. KöllingMThe greenfoot programming environmentACM Transactions on Computing Education (TOCE)201010414 Urquiza-FuentesJVelázquez-IturbideJÁA survey of successful evaluations of program visualization and algorithm animation systemsACM Transactions on Computing Education (TOCE)20099212110.1145/1538234.1538236 HarveyBComputer science logo style: Symbolic computing1997CambridgeMIT Press10.7551/mitpress/1972.001.0001 MaLFergusonJRoperMWoodMInvestigating the viability of mental models held by novice programmersACM SIGCSE Bulletin200739149950310.1145/1227504.1227481 QianYLehmanJStudents’ misconceptions and other difficulties in introductory programming: A literature reviewACM Transactions on Computing Education (TOCE)2017181110.1145/3077618 BersMUFlanneryLKazakoffERSullivanAComputational thinking and tinkering: Exploration of an early childhood robotics curriculumComputers & Education20147214515710.1016/j.compedu.2013.10.020 Moreno, A., Myller, N., Sutinen, E., & Ben-Ari, M. (2004). Visualizing programs with jeliot 3. In Proceedings of the Working Conference on Advanced Visual Interfaces (pp. 373–376). KelleherCPauschRLowering the barriers to programming: A taxonomy of programming environments and languages for novice programmersACM Computing Surveys (CSUR)20053728313710.1145/1089733.1089734 KöllingMQuigBPattersonARosenbergJThe bluej system and its pedagogyComputer Science Education200313424926810.1076/csed.13.4.249.17496 Ackermann, E., Gauntlett, D., & Foundation, C.W.L. (2009). Defining... systematic creativity. Retrieved from https://www.legofoundation.com/media/1078/systematic-creativity-report.pdf. Tsalapatas, H., Heidmann, O., Alimisi, R., & Houstis, E.N. (2012). Game-based programming towards developing algorithmic thinking skills in primary education. Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). Stem: Country comparisons: International comparisons of science, technology, engineering and mathematics (stem) education. Final report. HundhausenCDDouglasSAStaskoJTA meta-study of algorithm visualization effectivenessJournal of Visual Languages & Computing200213325929010.1006/jvlc.2002.0237 Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., Devlin, M., & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. In ACM Sigcse Bulletin, vol. 39 (pp. 204–223). ACM. BiggsJBCollisKFEvaluating the quality of learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome)2014CambridgeAcademic Press BaylissJDUsing games in introductory courses: Tips from the trenchesACM SIGCSE Bulletin200941133734110.1145/1539024.1508989 MarínBFrezJCruz-LemusJGeneroMAn empirical investigation on the benefits of gamification in programming coursesACM Transactions on Computing Education (TOCE)20181914 Watt, H. (2016). Session d: Promoting girls’ and boys’ engagement and participation in senior secondary stem fields and occupational aspirations. SorvaJKaravirtaVMalmiLA review of generic program visualization systems for introductory programming educationACM Transaction on Computing Education201310.1145/2490822 GrossmanPResearch on pedagogical approaches in teacher educationStudying teacher education2009USRoutledge437488 246_CR5 246_CR14 246_CR6 B Harvey (246_CR8) 1997 246_CR10 B Marín (246_CR15) 2018; 19 P Grossman (246_CR7) 2009 MU Bers (246_CR3) 2014; 72 246_CR1 M Kölling (246_CR12) 2010; 10 C Kelleher (246_CR11) 2005; 37 JB Biggs (246_CR4) 2014 CD Hundhausen (246_CR9) 2002; 13 246_CR24 246_CR22 246_CR20 JD Bayliss (246_CR2) 2009; 41 M Kölling (246_CR13) 2003; 13 L Ma (246_CR16) 2007; 39 J Sorva (246_CR21) 2013 J Urquiza-Fuentes (246_CR23) 2009; 9 JM Wing (246_CR25) 2006; 49 Y Qian (246_CR19) 2017; 18 246_CR17 246_CR18 |
| References_xml | – reference: Urquiza-FuentesJVelázquez-IturbideJÁA survey of successful evaluations of program visualization and algorithm animation systemsACM Transactions on Computing Education (TOCE)20099212110.1145/1538234.1538236 – reference: KöllingMThe greenfoot programming environmentACM Transactions on Computing Education (TOCE)201010414 – reference: MarínBFrezJCruz-LemusJGeneroMAn empirical investigation on the benefits of gamification in programming coursesACM Transactions on Computing Education (TOCE)20181914 – reference: BersMUFlanneryLKazakoffERSullivanAComputational thinking and tinkering: Exploration of an early childhood robotics curriculumComputers & Education20147214515710.1016/j.compedu.2013.10.020 – reference: Dehnadi, S. (2006). Testing programming aptitude. In PPIG, (p. 9). – reference: BiggsJBCollisKFEvaluating the quality of learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome)2014CambridgeAcademic Press – reference: da Silva, T.R., Medeiros, T., & da Silva Aranha, E.H. (2015). The use of games on the teaching of programming: A systematic review. In CIbSE (p. 474) – reference: Moreno, A., Myller, N., Sutinen, E., & Ben-Ari, M. (2004). Visualizing programs with jeliot 3. In Proceedings of the Working Conference on Advanced Visual Interfaces (pp. 373–376). – reference: WingJMComputational thinkingCommunications of the ACM2006493333510.1145/1118178.1118215 – reference: Tsalapatas, H., Heidmann, O., Alimisi, R., & Houstis, E.N. (2012). Game-based programming towards developing algorithmic thinking skills in primary education. – reference: KelleherCPauschRLowering the barriers to programming: A taxonomy of programming environments and languages for novice programmersACM Computing Surveys (CSUR)20053728313710.1145/1089733.1089734 – reference: GrossmanPResearch on pedagogical approaches in teacher educationStudying teacher education2009USRoutledge437488 – reference: Jawad, H.M., de Laski-Smith, D., & Tout, S. (2018). The code genie programming environment. In 2018 IEEE International Conference on Electro/Information Technology (EIT) (pp. 0163–0168). https://doi.org/10.1109/EIT.2018.8500194. – reference: Ackermann, E., Gauntlett, D., & Foundation, C.W.L. (2009). Defining... systematic creativity. Retrieved from https://www.legofoundation.com/media/1078/systematic-creativity-report.pdf. – reference: SorvaJKaravirtaVMalmiLA review of generic program visualization systems for introductory programming educationACM Transaction on Computing Education201310.1145/2490822 – reference: Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). Stem: Country comparisons: International comparisons of science, technology, engineering and mathematics (stem) education. Final report. – reference: Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., Devlin, M., & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. In ACM Sigcse Bulletin, vol. 39 (pp. 204–223). ACM. – reference: Siekmann, G., & Korbel, P. (2016). Defining stem skills: Review and synthesis of the literature. In NCVER. Commonwealth of Australia, AU. – reference: HundhausenCDDouglasSAStaskoJTA meta-study of algorithm visualization effectivenessJournal of Visual Languages & Computing200213325929010.1006/jvlc.2002.0237 – reference: MaLFergusonJRoperMWoodMInvestigating the viability of mental models held by novice programmersACM SIGCSE Bulletin200739149950310.1145/1227504.1227481 – reference: Watt, H. (2016). Session d: Promoting girls’ and boys’ engagement and participation in senior secondary stem fields and occupational aspirations. – reference: QianYLehmanJStudents’ misconceptions and other difficulties in introductory programming: A literature reviewACM Transactions on Computing Education (TOCE)2017181110.1145/3077618 – reference: KöllingMQuigBPattersonARosenbergJThe bluej system and its pedagogyComputer Science Education200313424926810.1076/csed.13.4.249.17496 – reference: HarveyBComputer science logo style: Symbolic computing1997CambridgeMIT Press10.7551/mitpress/1972.001.0001 – reference: BaylissJDUsing games in introductory courses: Tips from the trenchesACM SIGCSE Bulletin200941133734110.1145/1539024.1508989 – volume: 10 start-page: 14 issue: 4 year: 2010 ident: 246_CR12 publication-title: ACM Transactions on Computing Education (TOCE) – volume: 37 start-page: 83 issue: 2 year: 2005 ident: 246_CR11 publication-title: ACM Computing Surveys (CSUR) doi: 10.1145/1089733.1089734 – ident: 246_CR14 – ident: 246_CR10 doi: 10.1109/EIT.2018.8500194 – volume: 49 start-page: 33 issue: 3 year: 2006 ident: 246_CR25 publication-title: Communications of the ACM doi: 10.1145/1118178.1118215 – ident: 246_CR18 doi: 10.1145/1345375.1345441 – ident: 246_CR6 – start-page: 437 volume-title: Studying teacher education year: 2009 ident: 246_CR7 – year: 2013 ident: 246_CR21 publication-title: ACM Transaction on Computing Education doi: 10.1145/2490822 – volume-title: Evaluating the quality of learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome) year: 2014 ident: 246_CR4 – volume: 41 start-page: 337 issue: 1 year: 2009 ident: 246_CR2 publication-title: ACM SIGCSE Bulletin doi: 10.1145/1539024.1508989 – volume: 13 start-page: 259 issue: 3 year: 2002 ident: 246_CR9 publication-title: Journal of Visual Languages & Computing doi: 10.1006/jvlc.2002.0237 – volume: 13 start-page: 249 issue: 4 year: 2003 ident: 246_CR13 publication-title: Computer Science Education doi: 10.1076/csed.13.4.249.17496 – ident: 246_CR17 doi: 10.1145/989863.989928 – volume: 9 start-page: 1 issue: 2 year: 2009 ident: 246_CR23 publication-title: ACM Transactions on Computing Education (TOCE) doi: 10.1145/1538234.1538236 – ident: 246_CR20 – volume: 18 start-page: 1 issue: 1 year: 2017 ident: 246_CR19 publication-title: ACM Transactions on Computing Education (TOCE) doi: 10.1145/3077618 – ident: 246_CR22 – ident: 246_CR24 – volume: 19 start-page: 4 issue: 1 year: 2018 ident: 246_CR15 publication-title: ACM Transactions on Computing Education (TOCE) – ident: 246_CR1 – volume: 72 start-page: 145 year: 2014 ident: 246_CR3 publication-title: Computers & Education doi: 10.1016/j.compedu.2013.10.020 – volume-title: Computer science logo style: Symbolic computing year: 1997 ident: 246_CR8 doi: 10.7551/mitpress/1972.001.0001 – ident: 246_CR5 – volume: 39 start-page: 499 issue: 1 year: 2007 ident: 246_CR16 publication-title: ACM SIGCSE Bulletin doi: 10.1145/1227504.1227481 |
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| Title | Diogene-CT: tools and methodologies for teaching and learning coding |
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| Volume | 18 |
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