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From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Development of a Gamification Model for Personalized E-le...
Afvensu Enoch Ibisu · 2024-03-28 · via cs.HC updates on arXiv.org

This study designed a personality-based gamification model for E-learning systems. It also implemented the model and evaluated the performance of the gamification model implemented. These were with a view to developing a model for gamifying personalization of e-learning systems. Personalization requirements for motivational tendencies based on the Myers-Briggs Type Indicator (MBTI) and gamification elements were elicited for e-learning from existing literature and from education experts using interview and questionnaire. The gamification model for personalized e-learning was designed by mapping motivational tendencies to corresponding gamification elements using set theory and rendered using Unified modelling language (UML) tools. The model was implemented using Hypertext Markup Language for the front end, Hypertext Preprocessor (PHP) for the backend and Structured Query Language (SQL) for database on WordPress. The model was evaluated using appeal, emotion, user-centricity as well as satisfaction as engagement criteria, and clarity, error correction as well as feedback for educational usability. The results collected from the implemented system database and questionnaires administered to learners showed an average appeal rating of 4.3, an emotion rating of 4.5, a user-centricity rating of 4.4, and a satisfaction rating of 4.4 in terms of engagement on a 5.0 scale. The results also showed that clarity, error correction and feedback received an average rating of 3.9, 4.7, and 4.8 respectively on a 5.0 scale concerning educational usability. In addition, when comparing educational usability (4.5) to engagement (4.4), educational usability received slightly higher ratings. The study concluded that the gamification model for personalized e-learning was suitable for increasing learner motivation and engagement within the personalized e-learning environment.