Can guided decomposition help end-users write larger block-based programs? a mobile robot experiment
Block-based programming environments, already popular in computer science education, have been successfully used to make programming accessible to end-users in domains like robotics, mobile apps, and even DevOps. Most studies of these applications have examined small programs that fit within a singl...
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| Published in: | Proceedings of ACM on programming languages Vol. 6; no. OOPSLA2; pp. 233 - 258 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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31.10.2022
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| ISSN: | 2475-1421, 2475-1421 |
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| Abstract | Block-based programming environments, already popular in computer science education, have been successfully used to make programming accessible to end-users in domains like robotics, mobile apps, and even DevOps. Most studies of these applications have examined small programs that fit within a single screen, yet real-world programs often grow large, and editing these large block-based programs quickly becomes unwieldy. Traditional programming language features, like functions, allow programmers to decompose their programs. Unfortunately, both previous work, and our own findings, suggest that end-users rarely use these features, resulting in large monolithic code blocks that are hard to understand. In this work, we introduce a block-based system that provides users with a hierarchical, domain-specific program structure and requires them to decompose their programs accordingly. Through a user study with 92 users, we compared this approach, which we call guided program decomposition, to a traditional system that supports functions, but does not require decomposition. We found that while almost all users could successfully complete smaller tasks, those who decomposed their programs were significantly more successful as the tasks grew larger. As expected, most users without guided decomposition did not decompose their programs, resulting in poor performance on larger problems. In comparison, users of guided decomposition performed significantly better on the same tasks. Though this study investigated only a limited selection of tasks in one specific domain, it suggests that guided decomposition can benefit end-user programmers. While no single decomposition strategy fits all domains, we believe that similar domain-specific sub-hierarchies could be found for other application areas, increasing the scale of code end-users can create and understand. |
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| AbstractList | Block-based programming environments, already popular in computer science education, have been successfully used to make programming accessible to end-users in domains like robotics, mobile apps, and even DevOps. Most studies of these applications have examined small programs that fit within a single screen, yet real-world programs often grow large, and editing these large block-based programs quickly becomes unwieldy. Traditional programming language features, like functions, allow programmers to decompose their programs. Unfortunately, both previous work, and our own findings, suggest that end-users rarely use these features, resulting in large monolithic code blocks that are hard to understand. In this work, we introduce a block-based system that provides users with a hierarchical, domain-specific program structure and requires them to decompose their programs accordingly. Through a user study with 92 users, we compared this approach, which we call guided program decomposition, to a traditional system that supports functions, but does not require decomposition. We found that while almost all users could successfully complete smaller tasks, those who decomposed their programs were significantly more successful as the tasks grew larger. As expected, most users without guided decomposition did not decompose their programs, resulting in poor performance on larger problems. In comparison, users of guided decomposition performed significantly better on the same tasks. Though this study investigated only a limited selection of tasks in one specific domain, it suggests that guided decomposition can benefit end-user programmers. While no single decomposition strategy fits all domains, we believe that similar domain-specific sub-hierarchies could be found for other application areas, increasing the scale of code end-users can create and understand. |
| ArticleNumber | 133 |
| Author | Ritschel, Nico Holmes, Reid Shepherd, David C. Garcia, Ronald Fronchetti, Felipe |
| Author_xml | – sequence: 1 givenname: Nico orcidid: 0000-0001-5600-2978 surname: Ritschel fullname: Ritschel, Nico email: ritschel@cs.ubc.ca organization: University of British Columbia, Canada – sequence: 2 givenname: Felipe orcidid: 0000-0003-2104-6676 surname: Fronchetti fullname: Fronchetti, Felipe email: fronchettl@vcu.edu organization: Virginia Commonwealth University, USA – sequence: 3 givenname: Reid orcidid: 0000-0003-4213-494X surname: Holmes fullname: Holmes, Reid email: rtholmes@cs.ubc.ca organization: University of British Columbia, Canada – sequence: 4 givenname: Ronald orcidid: 0000-0002-0982-1118 surname: Garcia fullname: Garcia, Ronald email: rxg@cs.ubc.ca organization: University of British Columbia, Canada – sequence: 5 givenname: David C. orcidid: 0000-0003-2017-7842 surname: Shepherd fullname: Shepherd, David C. email: shepherdd@vcu.edu organization: Virginia Commonwealth University, USA |
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| Keywords | mobile robots block-based programming program decomposition |
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Wolber David (e_1_2_1_50_1) Chen Chiu-Liang (e_1_2_1_11_1) 2012 e_1_2_1_20_1 Weintrop David (e_1_2_1_46_1) 2018 e_1_2_1_24_1 e_1_2_1_45_1 e_1_2_1_22_1 e_1_2_1_43_1 e_1_2_1_49_1 e_1_2_1_26_1 e_1_2_1_47_1 Burger Benjamin (e_1_2_1_9_1) 2020 Ritschel Nico (e_1_2_1_38_1) 2020 e_1_2_1_31_1 e_1_2_1_8_1 e_1_2_1_6_1 e_1_2_1_12_1 Fowler Martin (e_1_2_1_16_1) 1999 e_1_2_1_10_1 e_1_2_1_33_1 e_1_2_1_2_1 Pradel Michael (e_1_2_1_37_1) 2015 e_1_2_1_39_1 Kennedy Cazembe (e_1_2_1_28_1) 2018 Gonçalves Mateus Carvalho (e_1_2_1_19_1) 2021 Paolacci Gabriele (e_1_2_1_36_1) 2010 Argall Brenna D (e_1_2_1_4_1) 2009 Rose Simon (e_1_2_1_40_1) 2017 e_1_2_1_23_1 Biggs Geoffrey (e_1_2_1_7_1) 2003 Fraser Neil (e_1_2_1_17_1) 2015 e_1_2_1_44_1 e_1_2_1_27_1 e_1_2_1_25_1 e_1_2_1_48_1 e_1_2_1_29_1 Pan Zengxi (e_1_2_1_35_1) 2010 Fraser Neil (e_1_2_1_18_1) 2013 Scaffidi Christopher (e_1_2_1_41_1) e_1_2_1_30_1 e_1_2_1_5_1 e_1_2_1_3_1 e_1_2_1_34_1 e_1_2_1_1_1 e_1_2_1_32_1 Harvey Brian (e_1_2_1_21_1) 2013 e_1_2_1_15_1 Siegwart Roland (e_1_2_1_42_1) 2011 Conway Matthew John (e_1_2_1_13_1) 1998 Dorn Brian James (e_1_2_1_14_1) |
| References_xml | – reference: Andrew Bragdon, Robert Zeleznik, Steven P Reiss, Suman Karumuri, William Cheung, Joshua Kaplan, Christopher Coleman, Ferdi Adeputra, and Joseph J LaViola Jr. 2010. Code Bubbles: A working set-based interface for code understanding and maintenance. In Proceedings of the Conference on Human Factors in Computing Systems (CHI). 2503–2512. – reference: Gabriele Paolacci, Jesse Chandler, and Panagiotis G Ipeirotis. 2010. Running experiments on amazon mechanical turk. Judgment and Decision making, 5, 5 (2010), 411–419. – reference: Universal Robots. 2013. PolyScope Manual. – reference: Beate Jost, Markus Ketterl, Reinhard Budde, and Thorsten Leimbach. 2014. Graphical programming environments for educational robots: Open roberta-yet another one? In International Symposium on Multimedia. 381–386. – reference: Thomas R. G. Green and Marian Petre. 1996. Usability analysis of visual programming environments: A ‘cognitive dimensions’ framework. Journal of Visual Languages & Computing, 7, 2 (1996), 131–174. – reference: Barbara Rita Barricelli, Fabio Cassano, Daniela Fogli, and Antonio Piccinno. 2019. End-user development, end-user programming and end-user software engineering: A systematic mapping study. Journal of Systems and Software, 149 (2019), 101–137. – reference: David Weintrop, Afsoon Afzal, Jean Salac, Patrick Francis, Boyang Li, David C Shepherd, and Diana Franklin. 2018. Evaluating CoBlox: A comparative study of robotics programming environments for adult novices. In Proceedings of the Conference on Human Factors in Computing Systems (CHI). 1–12. – reference: Sofia Charalampidou, Apostolos Ampatzoglou, and Paris Avgeriou. 2015. Size and cohesion metrics as indicators of the long method bad smell: An empirical study. In Proceedings of the International Conference on Predictive Models and Data Analytics in Software Engineering. 1–10. – reference: David Weintrop. 2019. Block-based programming in computer science education. Commun. ACM, 62, 8 (2019), 22–25. – reference: José Miguel Mota, Iván Ruiz-Rube, Juan Manuel Dodero, and Inmaculada Arnedillo-Sánchez. 2018. Augmented reality mobile app development for all. Computers & Electrical Engineering, 65 (2018), 250–260. – reference: Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. 2009. A survey of robot learning from demonstration. Robotics and autonomous systems, 57, 5 (2009), 469–483. – reference: Mateus Carvalho Gonçalves, Otávio Neves Lara, Raphael Winckler de Bettio, and André Pimenta Freire. 2021. End-user development of smart home rules using block-based programming: A comparative usability evaluation with programmers and non-programmers. Behaviour & Information Technology, 1–23. – reference: Amy J. Ko, Robin Abraham, Laura Beckwith, Alan Blackwell, Margaret Burnett, Martin Erwig, Chris Scaffidi, Joseph Lawrance, Henry Lieberman, and Brad Myers. 2011. The state of the art in end-user software engineering. ACM Computing Surveys (CSUR), 43, 3 (2011), 1–44. – reference: Brian Harvey, Daniel D Garcia, Tiffany Barnes, Nathaniel Titterton, Daniel Armendariz, Luke Segars, Eugene Lemon, Sean Morris, and Josh Paley. 2013. Snap!(build your own blocks). In Proceeding of the Technical Symposium on Computer Science Education (SIGCSE). 759–759. – reference: Martin Fowler. 1999. Refactoring: Improving the Design of Existing Code. Addison-Wesley. – reference: US Department of Labor. 2021. Occupational outlook handbook. https://www.bls.gov/ooh/. – reference: David Weintrop and Nathan Holbert. 2017. From blocks to text and back: Programming patterns in a dual-modality environment. In Proceeding of the Technical Symposium on Computer Science Education (SIGCSE). 633–638. – reference: Matthew John Conway. 1998. Alice: Easy-to-learn three-dimensional scripting for novices. Ph. D. Dissertation. University of Virginia. – reference: Maria Kallia and Sue Sentance. 2017. Computing teachers’ perspectives on threshold concepts: Functions and procedural abstraction. In Proceedings of the Workshop on Primary and Secondary Computing Education. 15–24. – reference: Gregorio Robles, Jesús Moreno-León, Efthimia Aivaloglou, and Felienne Hermans. 2017. Software clones in scratch projects: On the presence of copy-and-paste in computational thinking learning. In International Workshop on Software Clones (IWSC). 1–7. – reference: Christopher Scaffidi, Mary Shaw, and Brad Myers. 2005. The ‘55m end-user programmers’ estimate revisited. Institute for Software Research, International, Carnegie Mellon University. – reference: Cazembe Kennedy and Eileen T Kraemer. 2018. What are they thinking? Eliciting student reasoning about troublesome concepts in introductory computer science. In Proceedings of the Koli Calling International Conference on Computing Education Research. 1–10. – reference: Zengxi Pan, Joseph Polden, Nathan Larkin, Stephen Van Duin, and John Norrish. 2010. Recent progress on programming methods for industrial robots. In International Symposium on Robotics and ROBOTIK. 1–8. – reference: Michael Pradel and Koushik Sen. 2015. The good, the bad, and the ugly: An empirical study of implicit type conversions in JavaScript. In 29th European Conference on Object-Oriented Programming (ECOOP 2015). – reference: Geoffrey Biggs and Bruce MacDonald. 2003. A survey of robot programming systems. In Proceedings of the Australasian Conference on Robotics and Automation (ACRA). 1–3. – reference: Susan Wiedenbeck, Patti L Zila, and Daniel S McConnell. 1995. End-user training: An empirical study comparing on-line practice methods. In Proceedings of the Conference on Human Factors in Computing Systems (CHI). 74–81. – reference: Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza. 2011. Introduction to autonomous mobile robots. MIT press. – reference: Simon Rose, MP Jacob Habgood, and Tim Jay. 2017. An exploration of the role of visual programming tools in the development of young children’s computational thinking. Electronic journal of e-learning, 15, 4 (2017), pp297–309. – reference: Robert Holwerda and Felienne Hermans. 2018. A usability analysis of blocks-based programming editors using cognitive dimensions. In symposium on visual languages and human-centric computing (VL/HCC). 217–225. – reference: John Maloney, Mitchel Resnick, Natalie Rusk, Brian Silverman, and Evelyn Eastmond. 2010. The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10, 4 (2010), 1–15. – reference: Neil Fraser. 2015. Ten things we’ve learned from Blockly. 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| Title | Can guided decomposition help end-users write larger block-based programs? a mobile robot experiment |
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