Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

The Model for Introduction of Gamification into E-learning in Higher Education

The fact that each student has a different way of learning and processing information has long been recognised by educationalists. In the classroom, the benefits derived from delivering learning content in ways that match the student's learning style have also been identified. As new modes of delivery of learning content such as computer-assisted learning systems (e.g. eLearning) have become increasingly popular, research into these has also identified the benefits of tailoring learning content to learning styles. However, in games-based learning (GBL), the adaptation based on learning style to enhance the educational experience has not been well researched. For the purpose of this research, a game with three game modes has been developed: 1) non-adaptivity mode; 2) a mode that customises the game according to the student's learning style identified by using a learning style questionnaire; and 3) a mode that has an in-game adaptive system that dynamically and continuously adapts its content according to the student's interactions in the game.This paper discusses the term adaptivity in a GBL context and presents the results of an experimental study investigating the differences in learning effectiveness of the different game modes compared to a paper-based learning. The study was performed with 120 Higher Education students learning the database language SQL (Structured Query Language). The results show that the game developed, regardless of mode, produced better learning outcomes than those who learned from a textbook while adaptive GBL was better in terms of allowing learners to complete the tasks faster than the other two game versions. Systematic review of the literature presenting empirical evidence on adaptive GBL.Evaluation of an adaptive GBL RPG game based on the Felder-Silverman learning style.Learning effectiveness analysis on paper-based learning and learning through games.Results show the game groups improve learning effectiveness.Results show in-game adaptive game group has shortest completion times.

An application of adaptive games-based learning based on learning style to teach SQL

Dynamic difficulty adjustment (DDA) is a technique for adaptively changing a game to make it easier or harder. A common paradigm to achieve DDA is through heuristic prediction and intervention, adjusting game difficulty once undesirable player states (e.g., boredom or frustration) are observed. Without quantitative objectives, it is impossible to optimize the strength of intervention and achieve the best effectiveness. In this paper, we propose a DDA framework with a global optimization objective of maximizing a player's engagement throughout the entire game. Using level-based games as our example, we model a player's progression as a probabilistic graph. Dynamic difficulty reduces to optimizing transition probabilities to maximize a player's stay time in the progression graph. We have successfully developed a system that applies this technique in multiple games by Electronic Arts, Inc., and have observed up to 9% improvement in player engagement with a neutral impact on monetization.

Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games

Towards a system of customized video game mechanics based on player personality: Relating the Big Five personality traits with difficulty adaptation in a first-person shooter game

Stephen Karpinskyj

Papers

Video game personalisation techniques: A comprehensive survey