Stage-Based Generative Learning Object Model for Automated Content Adaptation

Abstract

This paper introduces a Stage-Based (SB) Generative Learning Object (GLO) model to specify the learning content. 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. The SB model implements the deep internal staging by allocating parameters and functions (objects) into predefined stages according to the given context. The essence of the approach is the SB de-activation and activation of the objects within the specification. That ensures the automatic SB generation and flexibility for adaptation. We analyze the SB model capabilities, the use scenarios and processes, present a case study and extended results of using and evaluation in the robot-oriented computer science education.

Modelling Subject Domain Causality for Learning Content Renewal

“…validity of the domain knowledge) is a decisive factor. IT-supported LO content renewal (adaptation) using a causal knowledge is promising, rational considering the changing business requirements and their impact on educational content (Stuikys et al, 2017).…”

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

“…The proposed conditional generator and the discriminator competed against each other, so that the generator learned to produce the result. Stuikys et al [228] introduced a stage-based generative learning object model with the capability of automatic generation and adaptation.…”