Service Design Handover to user experience design – a systematic literature review

Leinonen, Aarne; Roto, Virpi

doi:10.1016/j.infsof.2022.107087

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…There are multiple possible explanations for this. First, more expert programmers often do not necessarily need to read the code character by character or word by word when forming an understanding of the code, rather, they study features of the code that are relevant to the task at hand [18]. Consequently, a student may miss syntax errors or minor bugs, if they are not in focus.…”

Section: Discussionmentioning

confidence: 99%

The Implications of Large Language Models for CS Teachers and Students

MacNeil

Kim

Leinonen

et al. 2023

Proceedings of the 54th ACM Technical Symposium on Computer Science Education v. 2

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Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle. In certain conditions, buggy code may even exhibit correct behavior -in other cases, the issue might be about how a problem statement has been interpreted. Such errors can be hard to spot when reading the code, and they can also at times be missed by automated tests. There is great educational potential in automatically detecting logic errors, especially when paired with suitable feedback for novices. Large language models (LLMs) have recently demonstrated surprising performance for a range of computing tasks, including generating and explaining code. These capabilities are closely linked to code syntax, which aligns with the next token prediction behavior of LLMs. On the other hand, logic errors relate to the runtime performance of code and thus may not be as well suited to analysis by LLMs. To explore this, we investigate the performance of two popular LLMs, GPT-3 and GPT-4, for detecting and providing a novice-friendly explanation of logic errors. We compare LLM performance with a large cohort of introductory computing students (𝑛 = 964) solving the same error detection task. Through a mixed-methods analysis of student and model responses, we observe significant improvement in logic error identification between the previous and current generation of LLMs, and find that both LLM generations significantly outperform students.We outline how such models could be integrated into computing education tools, and discuss their potential for supporting students when learning programming.

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Section: Discussionmentioning

confidence: 99%

The Implications of Large Language Models for CS Teachers and Students

MacNeil

Kim

Leinonen

et al. 2023

Proceedings of the 54th ACM Technical Symposium on Computer Science Education v. 2

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“…As a result, a section of my study leans toward information studies, a direction I would have never thought of had I not talked to my colleague and informants, who had witnessed the importance of this topic in their work in the "real world." Later in my literature research, I found out that this topic could also be interesting academically (Leinonen & Roto, 2023). These "pivotal insights" (Charmaz, 2014, p. 18) are central in the iterative process of grounded theory research.…”

Section: Societal Impact Through Finding Meaningful and Original Rese...mentioning

confidence: 96%

Societal Impact Through Research Methodology: Constructivist Grounded Theory Method in Design Research

Juuti

2023

Blucher Design Proceedings

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The aim of this article is to explore, how the nature and extent of societal impact of design research is rooted in the very core of research practice -the research design and its epistemology. I propose that achieving societal impact in design research begins with the research method. Furthermore, I reflect on my personal experience of using the Constructivist Grounded Theory Method (CGTM) in my ongoing PhD study and describe what I view as the societal impact derived from my work. Using CGTM offers three angles to societal impact: Finding meaningful and impactful research challenges, creating substantial theories that have explanatory power for practitioners, and bridging the gap between academic research and practice. The quality of grounded theory is evaluated by credibility, originality, resonance, and usefulness. The same criteria can be used to enhance the potential of doctoral education in design to tackle societal problems.

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