Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
This study explores the reconstruction of the educational value of failure experiences in gamified learning,aiming to provide theoretical and practical guidance for optimizing learning processes.The research systemati...This study explores the reconstruction of the educational value of failure experiences in gamified learning,aiming to provide theoretical and practical guidance for optimizing learning processes.The research systematically analyzes the current status and problems of failure experiences in gamified learning,and clarifies their educational values in three dimensions:cognitive development,emotional attitudes,and social skills.Theoretically,failure experiences can stimulate learners’reflection,deepen knowledge understanding,cultivate critical thinking,enhance frustration tolerance,motivate learning,and improve social interaction abilities.Practically,strategies for reconstructing educational value are proposed,including goal-oriented design with contextual elements,cognitive guidance through failure education and role models,and interactive reinforcement via timely feedback and technological tools.Empirical results show that the experimental group using reconstructed strategies significantly outperforms the control group in learning motivation,academic performance,and attitudes toward gamified learning.The study fills academic gaps in gamified learning theories,offers actionable insights for educators and developers,and highlights the need for expanded sample scope,enhanced qualitative research,and long-term strategy validation in future studies.展开更多
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘This study explores the reconstruction of the educational value of failure experiences in gamified learning,aiming to provide theoretical and practical guidance for optimizing learning processes.The research systematically analyzes the current status and problems of failure experiences in gamified learning,and clarifies their educational values in three dimensions:cognitive development,emotional attitudes,and social skills.Theoretically,failure experiences can stimulate learners’reflection,deepen knowledge understanding,cultivate critical thinking,enhance frustration tolerance,motivate learning,and improve social interaction abilities.Practically,strategies for reconstructing educational value are proposed,including goal-oriented design with contextual elements,cognitive guidance through failure education and role models,and interactive reinforcement via timely feedback and technological tools.Empirical results show that the experimental group using reconstructed strategies significantly outperforms the control group in learning motivation,academic performance,and attitudes toward gamified learning.The study fills academic gaps in gamified learning theories,offers actionable insights for educators and developers,and highlights the need for expanded sample scope,enhanced qualitative research,and long-term strategy validation in future studies.
