The Relationship between Co-Creative Dialogue and High School Learners' Satisfaction with their Collaborator in Computational Music Remixing
Gloria Ashiya Katuka, Alexander R. Webber, Joseph B. Wiggins, Kristy Elizabeth Boyer, Brian Magerko, Tom McKlin, Jason Freeman
Abstract
Co-creative proccesses between people can be characterized by rich dialogue that carries each person's ideas into the collaborative space. When people co-create an artifact that is both technical and aesthetic, their dialogue reflects the interplay between these two dimensions. However, the dialogue mechanisms that express this interplay and the extent to which they are related to outcomes, such as peer satisfaction, are not well understood. This paper reports on a study of 68 high school learner dyads' textual dialogues as they create music by writing code together in a digital learning environment for musical remixing. We report on a novel dialogue taxonomy built to capture the technical and aesthetic dimensions of learners' collaborative dialogues. We identified dialogue act n-grams (sequences of length 1, 2, or 3) that are present within the corpus and discovered five significant n-gram predictors for whether a learner felt satisfied with their partner during the collaboration. The learner was more likely to report higher satisfaction with their partner when the learner frequently acknowledges their partner, exchanges positive feedback with their partner, and their partner proposes an idea and elaborates on the idea. In contrast, the learner is more likely to report lower satisfaction with their partner when the learner frequently accepts back-to-back proposals from their partner and when the partner responds to the learner's statements with positive feedback. This work advances understanding of collaborative dialogue within co-creative domains and suggests dialogue strategies that may be helpful to foster co-creativity as learners collaborate to produce a creative artifact. The findings also suggest important areas of focus for intelligent or adaptive systems that aim to support learners during the co-creative process.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c0d70b3f-b057-42a5-a0d6-6f5857d2dde8Related papers
- Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced ModelZhejing Hu, Yan Liu, Gong Chen, Xiao Ma et al.AAAI 2024 · 1 citation
- Understanding Women's Remote Collaborative Programming Experiences: The Relationship between Dialogue Features and Reported PerceptionsKimberly Michelle Ying, Fernando J. Rodríguez, Alexandra Lauren Dibble, Kristy Elizabeth BoyerCSCW 2020 · 14 citations
- Lost in Co-curation: Uncomfortable Interactions and the Role of Communication in Collaborative Music PlaylistsSo Yeon Park, Sang Won LeeCSCW 2021 · 10 citations
- Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative LearningTaufiq Daryanto, Xiaohan Ding, Kaike Ping, Lance T. Wilhelm et al.CHI 2026 · 2 citations
- Expressive Auditory Gestures in a Voice-Based Pedagogical AgentJessy Ceha, Edith LawCHI 2022 · 14 citations
