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Abstract
In recent years, research on collaborative interaction has relied on manual coding of rich audio/video recordings. The fine-grained analysis of such material is extremely time-consuming and labor-intensive. This is not only difficult to scale, but, as a result, might also limit the quality and completeness of coding due to fatigue, inherent human biases, (accidental or intentional), and inter-rater inconsistencies. In this paper, we explore how recent advances in machine learning may reduce manual effort and loss of information while retaining the value of human intelligence in the coding process. We present ACACIA (AI Chain for Augmented Collaborative Interaction Analysis), an AI video data analysis application which combines a range of advances in machine perception of video material for the analysis of collaborative interaction. We evaluate ACACIA’s abilities, show how far we can already get, and which challenges remain. Our contribution lies in establishing a combined machine and human analysis pipeline that may be generalized to different collaborative settings and guide future research.
Original language | English (American) |
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Article number | 571 |
Pages (from-to) | 354-380 |
Number of pages | 27 |
Journal | Proceedings of the ACM on Human-Computer Interaction |
Volume | 6 |
Issue number | ISS |
DOIs | |
Publication status | Published - 14 Nov 2022 |
Keywords
- artificial intelligence
- collaboration analysis
- data analysis
- empirical studies
- observational data
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HYCOS - Hybrid Collaboration Spaces
Augstein, M. (PI), Schönböck, J. (CoI), Hirschmann, F. (CoI), Kovacs, C. (CoI) & Neumayr, T. (CoI)
01.04.2022 → 13.03.2026
Project: Research Project