On-the-Fly Adaptive Planning for Game-Based Learning

摘要

In this paper, we present a model for competency development using serious games, which is underpinned by a hierarchical case-based planning strategy. In our model, a learner's objectives are addressed by retrieving a suitable learning plan in a two-stage retrieval process. First of all, a suitable abstract plan is retrieved and personalised to the learner's specific requirements. In the second stage, the plan is incrementally instantiated as the learner engages with the learning material. Each instantiated plan is composed of a series of stories - interactive narratives designed to improve the learner's competence within a particular learning domain. The sequence of stories in an instantiated plan is guided by the planner, which monitors the learner performance and suggests the next learning step. To create each story, the learner's competency proficiency and performance assessment history are considered. A new story is created to further progress the plan instantiation. The plan succeeds when the user consistently reaches a required level of proficiency. The successful instantiated plan trace is stored in an experience repository and forms a knowledge base on which introspective learning techniques are applied to justify and/or refine abstract plan composition.