Affective states in digital game-based learning: Thematic evolution and social network analysis

Area of Science:

• Educational Technology
• Psychology
• Computer Science

Background:

• Learner affect is strongly linked to learning outcomes, influencing well-being, quality, productivity, and interaction.
• Digital game-based learning (DGBL) is recognized for enhancing learning experiences and student motivation.
• The study of emotions in DGBL is a growing research area requiring a comprehensive overview.

Purpose of the Study:

• To explore current advances and topics in emotions within virtual DGBL across all educational levels.
• To provide a comprehensive understanding of the accumulated literature on emotions in DGBL.
• To identify the evolution and relationships between key topics in DGBL emotion research.

Main Methods:

• Systematic literature review of 393 research articles from Web of Science.
• Thematic evolution analysis to track topic development across four categories: games, emotions, applications, and analytical technologies.
• Social network analysis to examine co-occurrences and relationships between identified topics.

Main Results:

• Integration of diverse applications (e.g., mobile learning) and analytical technologies (e.g., learning analytics, affective computing) is increasing.
• A wider range of affective states, socio-emotional factors, and digital games are being investigated.
• Topic evolution shows interconnectedness between games, learner emotions, technological applications, and data analysis methods.

Conclusions:

• Future research should focus on children's anxiety and engagement in collaborative gameplay.
• Personalized support considering individual personalities and characteristics is crucial.
• Further investigation into emotion dynamics, multimodal data, game customization, and balancing challenge with learning anxiety is recommended.