A Domain-Independent, Extensible Framework for Generators of Training Game Activities on Declarative Knowledge

Background

Training the memorization of declarative knowledge requires the repetitive presentation of various forms of factual questions to learners. Educational games designed for this purpose should offer activities that are both tailored to individual learners and varied to prevent boredom. Whilst the Technology-Enhanced Learning (TEL) literature already suggests several techniques for implementing adaptations, the concept of generation remains underexplored, particularly when considering the adaptation of both educational and game dimensions simultaneously. Therefore, research focused on the design and implementation of ‘generators' as integral components of training games, which are responsible for creating varied and adapted training activities, remains pertinent.

Objectives

Our research aims to propose tools and techniques that facilitate the design and development of these generators from a computer science perspective.

Methods

We employed model driven engineering (MDE) theories and practises to initially characterise a generator as a model transformation that uses input models to produce an output model. We identified all these models through both a concrete case study and a literature review, with each model capturing a different facet of the information. By generalising the domain-specific aspects of the case study and selecting a game genre along with certain game design choices, we subsequently identified the metamodels that describe all these models and the generation rules.

Results and Conclusions

The proposed framework supports the design and implementation of Roguelite-oriented, adaptive, and varied activity generators for declarative knowledge (DK) training. It includes metamodels, models, code generation, and extension mechanisms to assist software engineers in addressing specific domains of declarative knowledge. By extending the framework, engineers are guided in the design process and can produce a software generator capable of generating adaptive and varied training activities in the form of dungeon levels within a Roguelite game. This framework is a domain-independent tool intended to support the development of training games targeting the acquisition of declarative knowledge. To illustrate its domain independence, the framework has been extended to multiple didactic domains. In this article, the domain of multiplication tables is used as a guiding thread, as it is directly connected to the AdapTABLES research project, which served as the primary driver for this work.