Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task Article Swipe
Mengxiao Zhu
,
Xin Wang
,
Xiantao Wang
,
Zihang Chen
,
Wei Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3686981
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3686981
Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored ways to make sense of the complex processes involved in collaborative problem solving. However, encoding explicit behaviors into subskills within the frameworks of CPS skills is still a challenging task. Traditional studies have relied on manual coding to decipher behavioral data for CPS, but such coding methods can be very time-consuming and cannot support real-time analyses. Scholars have begun to explore approaches for constructing automatic coding models. Nevertheless, the existing models built using machine learning or deep learning techniques depend on a large amount of training data and have relatively low accuracy. To address these problems, this paper proposes a prompt-based learning pre-trained model. The model can achieve high performance even with limited training data. In this study, three experiments were conducted, and the results showed that our model not only produced the highest accuracy, macro F1 score, and kappa values on large training sets, but also performed the best on small training sets of the CPS behavioral data. The application of the proposed prompt-based learning pre-trained model contributes to the CPS skills coding task and can also be used for other CSCW coding tasks to replace manual coding.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3686981
- https://dl.acm.org/doi/pdf/10.1145/3686981
- OA Status
- hybrid
- Cited By
- 3
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404172990
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404172990Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3686981Digital Object Identifier
- Title
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Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online TaskWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-07Full publication date if available
- Authors
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Mengxiao Zhu, Xin Wang, Xiantao Wang, Zihang Chen, Wei HuangList of authors in order
- Landing page
-
https://doi.org/10.1145/3686981Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3686981Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3686981Direct OA link when available
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Task (project management), Computer science, Mathematics education, Human–computer interaction, Psychology, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.models | 104 |
| abstract_inverted_index.relied | 67 |
| abstract_inverted_index.score, | 171 |
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| abstract_inverted_index.models. | 100 |
| abstract_inverted_index.problem | 1, 45 |
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| abstract_inverted_index.results | 158 |
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| abstract_inverted_index.solving | 2 |
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| abstract_inverted_index.support | 87 |
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| abstract_inverted_index.Scholars | 90 |
| abstract_inverted_index.decipher | 72 |
| abstract_inverted_index.encoding | 48 |
| abstract_inverted_index.existing | 103 |
| abstract_inverted_index.explicit | 49 |
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| abstract_inverted_index.proposed | 24, 197 |
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| abstract_inverted_index.accuracy. | 125 |
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| abstract_inverted_index.automatic | 98 |
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| abstract_inverted_index.processes | 41 |
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| abstract_inverted_index.21st-century | 11 |
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| abstract_inverted_index.Collaborative | 0 |
| abstract_inverted_index.Nevertheless, | 101 |
| abstract_inverted_index.collaborative | 44 |
| abstract_inverted_index.conceptualize | 30 |
| abstract_inverted_index.time-consuming | 84 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.91784214 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |