Abstract
In recent years, the XLogoOnline programming platform has gained popularity among novice learners. It integrates the Logo programming language with visual programming, providing a visual interface for learning computing concepts. However, XLogoOnline offers only a limited set of tasks, which are inadequate for learners to master the computing concepts that require sufficient practice. To address this, we introduce XLogoSyn, a novel technique for synthesizing high-quality tasks for varying difficulty levels. Given a reference task, XLogoSyn can generate practice tasks at varying difficulty levels that cater to the varied needs and abilities of different learners. XLogoSyn achieves this by combining symbolic execution and constraint satisfaction techniques. Our expert study demonstrates the effectiveness of XLogoSyn. We have also deployed synthesized practice tasks into XLogoOnline, highlighting the educational benefits of these synthesized practice tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Implementation of XLogoSyn is publicly available at:
https://github.com/machine-teaching-group/aied2024-xlogo-tasksyn.
References
Hromkovič, J., Serafini, G., Staub, J.: XLogoOnline: a single-page, browser-based programming environment for schools aiming at reducing cognitive load on pupils. In: Dagiene, V., Hellas, A. (eds.) ISSEP 2017. LNCS, vol. 10696, pp. 219–231. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71483-7_18
XLogoOnline. XLogoOnline Platform (2023). https://xlogo.inf.ethz.ch/
Pea, R.D.: Logo programming and problem solving (1987)
Staub, J.: Logo environments in the focus of time. Bull. EATCS 133 (2021). https://dblp.org/rec/journals/eatcs/Staub21.html?view=bibtex
Smith, A.M., Mateas, M.: Answer set programming for procedural content generation: a design space approach. IEEE Trans. Comput. Intell. AI Games 3(3), 187–200 (2011)
Park, K., Mott, B., Min, W., Wiebe, E., Boyer, K.E., Lester, J.: Generating game levels to develop computer science competencies in game-based learning environments. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 240–245. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_44
Polozov, O., O’Rourke, E., Smith, A.M., Zettlemoyer, L., Gulwani, S., Popovic, Z.: Personalized mathematical word problem generation. In: IJCAI (2015)
Alvin, C., Gulwani, S., Majumdar, R., Mukhopadhyay, S.: Synthesis of geometry proof problems. In: AAAI (2014)
de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24
Ahmed, U.Z., et al.: Synthesizing tasks for block-based programming. In: NeurIPS (2020)
Ghosh, A., Tschiatschek, S., Devlin, S., Singla, A.: Adaptive scaffolding in block-based programming via synthesizing new tasks as pop quizzes. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education. AIED 2022, vol. 13355, pp. 28–40. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_3
Padurean, V.-A., Tzannetos, G., Singla, A.: Neural task synthesis for visual programming. Trans. Mach. Learn. Res. (2023). https://www.jmlr.org/tmlr/papers/bib/aYkYajcJDN.bib
OpenAI. ChatGPT (2023). https://openai.com/blog/chatgpt
Denny, P., et al.: Generative AI for education (GAIED): advances, opportunities, and challenges. CoRR, abs/2402.01580 (2024)
Sarsa, S., Denny, P., Hellas, A., Leinonen, J.: Automatic generation of programming exercises and code explanations using large language models. In: ICER (2022)
Phung, T., et al.: Generative AI for programming education: benchmarking ChatGPT, GPT-4, and human tutors. In: ICER V.2 (2023)
Singla, A.: Evaluating ChatGPT and GPT-4 for visual programming. In: ICER V.2 (2023)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Cochran, W.G.: The \(\chi \)2 test of goodness of fit. Ann. Math. Stat. 23(3), 315–345 (1952)
Acknowledgments
Funded/Co-funded by the European Union (ERC, TOPS, 101039090). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wen, C., Ghosh, A., Staub, J., Singla, A. (2024). Task Synthesis for Elementary Visual Programming in XLogoOnline Environment. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-64312-5_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64311-8
Online ISBN: 978-3-031-64312-5
eBook Packages: Computer ScienceComputer Science (R0)