Stage-Based Generative Learning Object Model to Support Automatic Content Generation and Adaptation
Vytautas Štuikys, Renata Burbaitė, Kristina Bespalova, Vida Drąsutė, Giedrius Ziberkas, Algimantas Venčkauskas
- Year
- 2016
- Citations
- 5
Abstract
The paper introduces a novel Generative Learning Object (GLO) model, the Stage-Based Model (SBM) to specify the learning content. New capabilities of the model are the content automatic generation and adaptation. Externally, our model has a similar structure as the known two-level generic models (i.e. metadata and content implementation). The internal structure, however, is quite different in both parts. The use of the external parameterization technology based on pre-programming predefines the internal structure. Furthermore, the structure is derived from the initial parameterized GLO model using the refactoring tool. The technology we use in both models is based on the parameter-function relationship so that to perform manipulations on parameters. Parameters represent metadata, while the relationship implements the content variability by pre-programming the possible changes in advance so that to create the space for adaptation. The SBM implements deep internal staging by allocating parameters and functions (further objects) into predefined stages according to the given context. For example, pedagogically related parameters (objectives, teaching model, etc.) have the highest priority and appear at the top stage while the others - at the remaining stages. Typically, objects in the initial GLO specification are active, i.e. are ready to perform the prescribed role when interpreted by adequate tools. In SBM, the top stage objects are active while the remaining are passive (not able to serve the prescribed role there). The essence of the approach is the stage-based de-activation and activation of the objects within the pre-programmed specification. That ensures the automatic stage-based generation and flexibility for adaptation. In this paper, we analyze the SBM capabilities, present a case study and extended results of using the approach to the robot-oriented teaching in computer science. We also provide the pedagogical evaluation of the approach.
Keywords
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