4 Search Results for "Ko, Amy"

Document
DOI: 10.4230/OASIcs.Programming.2025.14
PShapeTrace: Linking Drawing Instructions with Visual Outcomes in Processing

Authors: Takashi Ishio and Yuta Yamasaki

Published in: OASIcs, Volume 134, Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)

Abstract
Processing is a Java-based programming language designed to facilitate learning programming through visual arts and creative coding. However, beginners must simultaneously learn both the programming language itself and image-processing concepts such as coordinate systems, making it challenging to understand the correspondence between drawing instructions and their visual outcomes. To help beginners analyze the drawing process in their code, this study proposes a tool named PShapeTrace that observes the execution of Processing programs and visualizes the relationship between drawing instructions and their results. A user study was conducted to evaluate the tool. Participants reported that the tool was useful for completing programming tasks. The resulting System Usability Scale (SUS) score was 72.75, indicating acceptable usability.
Cite as

Takashi Ishio and Yuta Yamasaki. PShapeTrace: Linking Drawing Instructions with Visual Outcomes in Processing. In Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025). Open Access Series in Informatics (OASIcs), Volume 134, pp. 14:1-14:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ishio_et_al:OASIcs.Programming.2025.14,
  author =	{Ishio, Takashi and Yamasaki, Yuta},
  title =	{{PShapeTrace: Linking Drawing Instructions with Visual Outcomes in Processing}},
  booktitle =	{Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)},
  pages =	{14:1--14:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-382-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{134},
  editor =	{Edwards, Jonathan and Perera, Roly and Petricek, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Programming.2025.14},
  URN =		{urn:nbn:de:0030-drops-242982},
  doi =		{10.4230/OASIcs.Programming.2025.14},
  annote =	{Keywords: Traceability, dynamic analysis, graphical user interface}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.5
Integrating Questions About Learners’ Code in an Automated Assessment System

Authors: Afonso B. Caniço and André L. Santos

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
Questions about Learners' Code (QLCs) assess programming students' program comprehension skills by providing personalised questions targeting the students' own program code. We conducted a preliminary, experimental implementation of integrating QLCs in the Automated Assessment System (AAS) used in an introductory programming course using Java. QLCs targeted some of the code assignments which students had to complete during the course. We collected 889 answers to QLCs, answered by 13 students over five course modules. We found that as the complexity of exercises increases, the success rate of the same type of QLC may not improve, and even exhibit a decline over time. We further analysed incorrect answers individually to relate them to possible misconceptions.
Cite as

Afonso B. Caniço and André L. Santos. Integrating Questions About Learners’ Code in an Automated Assessment System. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{canico_et_al:OASIcs.ICPEC.2025.5,
  author =	{Cani\c{c}o, Afonso B. and Santos, Andr\'{e} L.},
  title =	{{Integrating Questions About Learners’ Code in an Automated Assessment System}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{5:1--5:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.5},
  URN =		{urn:nbn:de:0030-drops-240353},
  doi =		{10.4230/OASIcs.ICPEC.2025.5},
  annote =	{Keywords: programming education, student assessment, program comprehension, questions about learners’ code}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.3
Knowledge Engineering Using Large Language Models

Authors: Bradley P. Allen, Lise Stork, and Paul Groth

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.
Cite as

Bradley P. Allen, Lise Stork, and Paul Groth. Knowledge Engineering Using Large Language Models. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{allen_et_al:TGDK.1.1.3,
  author =	{Allen, Bradley P. and Stork, Lise and Groth, Paul},
  title =	{{Knowledge Engineering Using Large Language Models}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:19},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.3},
  URN =		{urn:nbn:de:0030-drops-194777},
  doi =		{10.4230/TGDK.1.1.3},
  annote =	{Keywords: knowledge engineering, large language models}
}
Document
DOI: 10.4230/DagRep.12.6.1
Theories of Programming (Dagstuhl Seminar 22231)

Authors: Thomas D. LaToza, Amy Ko, David C. Shepherd, Dag Sjøberg, and Benjamin Xie

Published in: Dagstuhl Reports, Volume 12, Issue 6 (2023)

Abstract
Much of computer science research focuses on techniques to make programming easier, better, less error prone, more powerful, and even more just. But rarely do we try to explain any of these challenges. Why is programming hard? Why is it slow? Why is it error prone? Why is it powerful? How does it do harm? These why and how questions are what motivated the Dagstuhl Seminar 22231 on Theories of Programming. This seminar brought together 28 CS researchers from domains most concerned with programming human and social activities: software engineering, programming languages, human-computer interaction, and computing education. Together, we sketched new theories of programming and considered the role of theories more broadly in programming.
Cite as

Thomas D. LaToza, Amy Ko, David C. Shepherd, Dag Sjøberg, and Benjamin Xie. Theories of Programming (Dagstuhl Seminar 22231). In Dagstuhl Reports, Volume 12, Issue 6, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{latoza_et_al:DagRep.12.6.1,
  author =	{LaToza, Thomas D. and Ko, Amy and Shepherd, David C. and Sj{\o}berg, Dag and Xie, Benjamin},
  title =	{{Theories of Programming (Dagstuhl Seminar 22231)}},
  pages =	{1--13},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{6},
  editor =	{LaToza, Thomas D. and Ko, Amy and Shepherd, David C. and Sj{\o}berg, Dag and Xie, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.6.1},
  URN =		{urn:nbn:de:0030-drops-174533},
  doi =		{10.4230/DagRep.12.6.1},
  annote =	{Keywords: computing education, human-computer interaction, programming languages, software engineering, theories of programming}
}
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