Predictive Modeling of Player Drop-Off Using Ensemble Machine Learning Techniques
Linda Miller 2025-02-02

Predictive Modeling of Player Drop-Off Using Ensemble Machine Learning Techniques

Thanks to Linda Miller for contributing the article "Predictive Modeling of Player Drop-Off Using Ensemble Machine Learning Techniques".

Predictive Modeling of Player Drop-Off Using Ensemble Machine Learning Techniques

This paper explores the role of artificial intelligence (AI) in personalizing in-game experiences in mobile games, particularly through adaptive gameplay systems that adjust to player preferences, skill levels, and behaviors. The research investigates how AI-driven systems can monitor player actions in real-time, analyze patterns, and dynamically modify game elements, such as difficulty, story progression, and rewards, to maintain player engagement. Drawing on concepts from machine learning, reinforcement learning, and user experience design, the study evaluates the effectiveness of AI in creating personalized gameplay that enhances user satisfaction, retention, and long-term commitment to games. The paper also addresses the challenges of ensuring fairness and avoiding algorithmic bias in AI-based game design.

This study investigates how mobile games can encourage physical activity among players, focusing on games that incorporate movement and exercise. It evaluates the effectiveness of these games in promoting health and fitness.

This study explores the impact of augmented reality (AR) technology on player immersion and interaction in mobile games. The research examines how AR, which overlays digital content onto the physical environment, enhances gameplay by providing more interactive, immersive, and contextually rich experiences. Drawing on theories of presence, immersion, and user experience, the paper investigates how AR-based games like Pokémon GO and Ingress engage players in real-world exploration, socialization, and competition. The study also considers the challenges of implementing AR in mobile games, including hardware limitations, spatial awareness, and player safety, and provides recommendations for developers seeking to optimize AR experiences for mobile game audiences.

This paper investigates how different motivational theories, such as self-determination theory (SDT) and the theory of planned behavior (TPB), are applied to mobile health games that aim to promote positive behavioral changes in health-related practices. The study compares various mobile health games and their design elements, including rewards, goal-setting, and social support mechanisms, to evaluate how these elements align with motivational frameworks and influence long-term health behavior change. The paper provides recommendations for designers on how to integrate motivational theory into mobile health games to maximize user engagement, retention, and sustained behavioral modification.

This research explores the role of big data and analytics in shaping mobile game development, particularly in optimizing player experience, game mechanics, and monetization strategies. The study examines how game developers collect and analyze data from players, including gameplay behavior, in-app purchases, and social interactions, to make data-driven decisions that improve game design and player engagement. Drawing on data science and game analytics, the paper investigates the ethical considerations of data collection, privacy issues, and the use of player data in decision-making. The research also discusses the potential risks of over-reliance on data-driven design, such as homogenization of game experiences and neglect of creative innovation.

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