Sophie Callies, Mathieu Gravel, É. Beaudry, J. Basque
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引用次数: 5
摘要
本文提出了一种为模拟严肃游戏设计的体系结构,该体系结构自动生成适应学习者学习进程的基于游戏的场景。我们提出了该体系结构的三个中心模块:(1)学习者模型,(2)适应模块和(3)日志模块。学习者模型估计游戏中目标技能的发展进程。适应模块使用这种估计来自动规划一个优化学习的游戏情境的适应序列。我们在Game of Homes中实现了我们的架构,这是一款模拟严肃游戏,旨在训练成年人的房地产基础知识。我们创建了基于脚本的Game of Homes版本,以便比较基于脚本的场景与生成的场景对学习进程的影响。我们定性地分析了36名玩了90分钟“家庭游戏”的成年人的日志文件。主要结果突出了为每个学习者生成的教学场景的特殊性,更具体地说,是在整个游戏过程中所提供的指导和学习内容的呈现的优化。
Logs Analysis of Adapted Pedagogical Scenarios Generated by a Simulation Serious Game Architecture
This paper presents an architecture designed for simulation serious games, which automatically generates game-based scenarios adapted to learner's learning progression. We present three central modules of the architecture: (1) the learner model, (2) the adaptation module and (3) the logs module. The learner model estimates the progression of the development of skills targeted in the game. The adaptation module uses this estimation to automatically plan an adapted sequence of in-game situations optimizing learning. We implemented our architecture in Game of Homes, a simulation serious game, which aims to train adults the basics of real estate. We built a scripted-based version of Game of Homes in order to compare the impact of scripted-based scenarios versus generated scenarios on learning progression. We qualitatively analyzed logs files of thirty-six adults who played Game of Homes for 90 minutes. The main results highlighted the specificity of the generated pedagogical scenarios for each learner and, more specifically, the optimization of the guidance provided and of the presentation of the learning content throughout the game.