Enterprise AI Analysis
Learner emotions in collaborative learning: bibliometrics with topic modelling
This paper presents a bibliometric and topic modeling analysis of 1866 articles on learner emotions in collaborative learning from 2000 to 2023. It identifies key research topics, their evolution, contributing countries/regions/institutions, and scientific collaborations. The study found a steady increase in research, particularly in online/computer-supported collaborative learning, and highlighted top topics like Digital learning platforms, Professional development of teachers, and Game-based learning. A conceptual framework for future research on emotional experiences in collaborative learning is proposed.
Executive Impact & Key Metrics
Our AI-driven analysis of "Learner emotions in collaborative learning: bibliometrics with topic modelling" reveals critical insights into the academic landscape and research trends, offering a strategic advantage for institutions and researchers.
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Total Articles Analyzed
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Years Covered
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Leading Journal's Articles
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Top Country's Articles
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Topic Modelling Findings
Bibliometric Performance Analysis
Scientific Collaboration Patterns
Conceptual Framework for Research
9.44%
Most Discussed Topic: Digital Learning Platforms
Dominant Research Themes
The analysis identified four most-discussed topics: Digital learning platforms (9.44%), Professional development of teachers (9.37%), Game-based learning (7.72%), and Interdisciplinary clinical practices (7.30%). Seven other topics had a proportion above 6%, including Interactive science learning, Creative interactions in education and society, Multifaceted aspects of healthcare and collaboration, Educational methods and academic metrics, Education for sustainable development, Multilingual education and TELL, and Wiki-based learning. Less attention was given to CSCL during the pandemic, Technology acceptance, Emotional, cognitive, and social facets of learning and regulation, and Collaborative learning methods. Overall, these topics reflect broad interests in learner emotions within collaborative learning from 2000 to 2023.
Evolving Research Trends
Digital learning platforms, Professional development of teachers, and CSCL during the pandemic showed a significant increasing tendency. Wiki-based learning was primarily researched from 2003-2009, while Game-based learning and Interactive science learning were also early focus areas. CSCL during the pandemic emerged as a research hotspot later, since 2008.
475
Total Journals Publishing Relevant Articles
Leading Journals
The research is interdisciplinary, with 475 journals contributing. Top journals include Computers & Education (94 articles), Education and Information Technologies (56), British Journal of Educational Technology (50), Interactive Learning Environments (42), and Australasian Journal of Educational Technology (39). Specialized journals like BMC Medical Education and Journal of Chemical Education also contribute, showcasing the broad applicability across domains.
Key Contributing Countries/Regions
Contributions are global, with the USA leading with 605 articles, followed by the UK (195), Australia (154), Spain (126), China (125), and Taiwan, Province of China (112). Other significant contributors include Canada (98), Netherlands (73), Hong Kong, China (58), Finland (54), Turkey (51), South Korea (47), Germany (42), Sweden (32), and Ireland (31).
Top Institutions
The University of Washington is the leading institutional contributor with 25 articles. Other significant institutions include the University of Hong Kong (23), Indiana University Bloomington (23), Beijing Normal University (23), University of Oulu (21), and National Taiwan Normal University (21). This highlights widespread academic engagement in understanding learner emotions in collaborative learning.
Enterprise Process Flow
Define collaboration by co-authorship
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Quantify co-occurrence frequency (countries/institutions)
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Visualize partnerships (Gephi)
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Identify global/regional collaboration trends
| Type | Collaborative Partners | Frequency |
|---|---|---|
| Regional | Canada-USA | 24 |
| Regional | China-USA | 19 |
| Regional | UK-USA | 13 |
| Regional | Australia-USA | 13 |
| Regional | Australia-UK | 11 |
| Regional | Taiwan, Province of China-China | 11 |
| Regional | Hong Kong-China | 11 |
| Institutional | Yuan Ze University-National Central University | 4 |
| Institutional | Beijing University of Posts and Telecommunications-Beijing Normal University | 4 |
| Institutional | Mississippi State University-University of Macau | 4 |
| Institutional | University of Utrecht–Open University of the Netherlands | 4 |
Insights on Collaboration
International collaborations are significant, with the USA, UK, Australia, and China being prominent. Regional and institutional collaborations tend to be higher among closely located entities, likely due to ease of communication. The study emphasizes the need for increased cross-regional collaborations to address evolving challenges in the field.
The Proposed 7-Dimension Framework
The study proposes a conceptual framework with seven key dimensions to guide research on emotional experiences in collaborative learning: contexts, emotional dimensions, collaborative methods and approaches, moderating and mediating variables, outcomes, subject domains, and theoretical foundations. This framework aims to provide a structured approach for scholars to obtain deeper insights and design emotionally supportive learning environments. For example, understanding how digital learning platforms or interdisciplinary groupings affect emotions (stress, anxiety, trust, frustration) and motivation (excitement, joy) is crucial. Effective emotion regulation strategies (mindfulness, support systems) and the role of technologies (wikis, games) are also highlighted.
Future Research Directions
Future research should delve deeper into how learner emotions are shaped by various contexts (e.g., hybrid models, interdisciplinary approaches), and how individual differences (e.g., cognitive style, tech familiarity) moderate emotional experiences. Investigations into the impact of emotions on academic performance and career readiness, especially in specific subject domains like science, healthcare, and language education, are needed. Integrating theoretical foundations such as emotion regulation theory and social and emotional learning (SEL) will provide robust frameworks for understanding these dynamics. Leveraging AI for sentiment analysis and real-time feedback is also a promising avenue.
Projected AI ROI in Educational Research
Implementing AI for bibliometric analysis and topic modeling in educational research can significantly enhance efficiency and insight generation. Use the calculator below to estimate potential returns for your enterprise.
Annual Savings
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Annual Hours Reclaimed
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Your AI Research Assistant Implementation Roadmap
A phased approach to integrating AI for advanced research analysis, ensuring a smooth transition and maximum impact.
Phase 1: Data Ingestion & Pre-processing Automation
Automate the collection, cleaning, and structuring of academic literature from databases like Web of Science. AI-powered tools will streamline stop word removal, term consolidation, and singular form conversion, reducing manual effort significantly.
Phase 2: Advanced Topic Modeling & Trend Analysis
Deploy advanced STM for identifying latent research topics and their evolution. This phase includes fine-tuning models (e.g., selecting optimal topic numbers based on semantic coherence and exclusivity) and applying statistical trend analysis (Mann-Kendall test) to reveal developmental patterns.
Phase 3: Bibliometric & Collaboration Network Visualization
Implement tools like Gephi for social network analysis to visualize scientific collaborations among institutions and countries. Automate the generation of performance analyses for top journals, authors, and regions, providing clear insights into the research landscape.
Phase 4: Conceptual Framework Integration & Predictive Analytics
Integrate the identified conceptual framework into AI systems to guide future research and generate hypotheses. Develop predictive models for emerging research interests and potential collaboration opportunities based on historical data patterns.
Phase 5: Real-time Insights & Adaptive Recommendations
Develop an AI-driven dashboard that provides real-time insights into new publications, emerging topics, and influential researchers. The system will offer adaptive recommendations for collaborations and research focus areas, fostering proactive research strategies.
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