Dynamic Content Alteration For Generalized Use Cases
Keywords:
Item response theory, Artificial intelligence, Ma- chine learning, Procedural content generation, Behavior analysis, Dynamic content alteration, Personalized learning, Assessment efficiency, Assessment accuracy, Learner engagement, Learner motivation, Individualized assessment, Pre-test/post-test design, Randomized controlled trial, Multiple-choice questions, Difficulty score, Test item selection, Learning analytics, Data-driven assess- ment, Educational technology.Abstract
This abstract discusses two related topics: dynamic content alteration for skill assessment and dynamic difficulty adjustment for VR games.
The first topic focuses on a dynamic content alteration model for skill assessment in educational settings. Traditional assess- ments often lack personalization and fail to consider individual learners’ unique characteristics, leading to unequal outcomes and unfair evaluations. To address this, the proposed model utilizes behavior analysis and a decision tree algorithm to create personalized exams that adapt to the strengths and weaknesses of each learner. The model’s effectiveness is validated through a randomized controlled trial, demonstrating improved outcomes compared to static exams. Learners report feeling that the exams were tailored to their abilities, promoting a sense of equity and fairness. The model also shows potential for personalized course design based on learners’ assessments.
The second topic explores dynamic difficulty adjustment (DDA) in virtual reality (VR) games. DDA is a technique used to adapt the game’s level of challenge based on the player’s skills, preferences, and performance. In VR games, DDA enhances the player’s experience by adjusting various game elements. This includes adaptive enemy behavior, where enemies become more challenging for skilled players and easier for struggling players. DDA can also dynamically adjust puzzle complexity and level design in response to the player’s proficiency, ensuring an engaging and balanced gameplay experience. Procedural content generation techniques further enable the generation of fresh challenges based on the player’s performance and preferences.
Both topics share a common objective of providing person- alized and adaptive experiences. While the first topic focuses on skill assessment in education, the second topic emphasizes enhancing player engagement and satisfaction in VR games. These areas of research demonstrate the potential of dynamic alteration techniques to create equitable assessments and im- mersive gaming experiences that align with individuals’ unique abilities and preferences.
