VLDB 2026 Research / reviewers in the wild / expert
Yuhei Oomachi
dblp:341/1637
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0001-6051-3658ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Observation and Evaluation for Individual Student Using Learning Analytics of Software Programing and Functional QuestionnaireabstractThis study assessed the impact of programming education on self-efficacy and motivation among elementary school students using learning analytics and functional questionnaire surveys. By analyzing the behavioral patterns for elemental school students in programming tasks related to basic geometric shapes through learning analytics, we identified the frequency of Run/Execute operations as a differentiating factor between students who completed all tasks and those who did not. Furthermore, employing a uniquely designed questionnaire allowed us to extract clear differences in motivation and achievement scales, as well as self-efficacy scales, between students who improved their learning outcomes and those who did not. The integration of learning analytics and functional questionnaires into elementary programming education emerged as a crucial tool for understanding and enhancing students' learning experiences, indicating the significance of adaptive educational strategies in the digital age. Motoi Nakao, Yuhei Oomachi |
SERA | 2 |
| 2024 | Extraction and Application of Introspection using Lego® Serious Play® Combined with Software MethodabstractThis study examines the potential of the LEGO® Serious Play® (LSP) method, augmented with generative artificial intelligence (AI), to facilitate introspection and its application across various settings. Employing a novel approach, we integrate LSP with transcription and generative AI to conduct one-on-one interviews aimed at extracting and analyzing introspective insights from participants. Our comparative analysis focuses on the effectiveness of this integrated method against conventional interview techniques, particularly in revealing less consciously acknowledged aspects of personality. The findings indicate that the use of LSP in combination with AI technologies provides a unique avenue for uncovering deeper, often subconscious, personal insights, as demonstrated by a distinctive pattern in the recognition of extracted keywords between methods. This research highlights the value of blending analog and digital methodologies to access and apply introspective data, offering significant implications for enhancing personal development and understanding. Motoi Nakao, Yuhei Oomachi, Michiko Matsuda |
SERA | 2 |