Modeling, Assessing, and Supporting Key Competencies Within Game Environments
Modeling, Assessing, and Supporting Key Competencies Within Game Environments
复制标题
建模、评估和支持游戏环境中的关键能力
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Chen
中科院分区：
文献类型：
--
作者：
V. Shute;Iskandaria Masduki;O. Donmez;V. Dennen;Y. Kim;Allan Jeong;Chen
实现任何成功的教学系统目标的第一个挑战取决于准确地识别特定学习者或学习者群体的特征，例如特定知识、技能和其他属性的类型和水平。第二个挑战是利用这些信息来改善学习。本章旨在通过描述一种分析关键能力和开发嵌入沉浸式游戏的有效评估的方法，扩展当前关于(a)教育上有价值的技能和(b)教学系统设计的思考。具体来说，我们将描述与隐身评估、诊断和教学决策相关的理论研究，并在沉浸式游戏环境中进行操作。隐性评估和诊断发生在学习（游戏）过程中，教学决策是基于对学习者当前和预期能力状态的推断。推断——诊断和预测——将由贝叶斯网络处理，并直接用于学生模型中，通过概率推断来处理不确定性，以更新和提高对学习者能力的信念值。结果的概率为决策提供信息，例如，根据学习者的当前状态选择教学支持。
The first challenge of accomplishing the goals of any successful instructional system depends on accurately identifying characteristics of a particular learner or group of learners – such as the type and level of specific knowledge, skills, and other attributes. The second challenge is then leveraging the information to improve learning. This chapter is intended to extend current thinking about (a) educationally valuable skills and (b) instructional system design by describing an approach for analyzing key competencies and developing valid assessments embedded within an immersive game. Specifically, we will describe theoretically based research relating to stealth assessment, diagnosis, and instructional decisions, operational within an immersive game environment. Stealth assessment and diagnosis occur during the learning (playing) process, and instructional decisions are based on inferences of learners’ current and projected competency states. Inferences – both diagnostic and predictive – will be handled by Bayesian networks and used directly in student models to handle uncertainty via probabilistic inference to update and improve belief values on learner competencies. Resulting probabilities inform decision making, as needed in, for instance, the selection of instructional support based on the learner’s current state.