| Title |
Stage-based generative learning object model for automated content adaptation / |
| Authors |
Štuikys, Vytautas ; Burbaitė, Renata ; Bespalova, Kristina ; Blažauskas, Tomas ; Barisas, Dominykas |
| DOI |
10.22364/bjmc.2017.5.2.03 |
| Full Text |
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| Is Part of |
Baltic journal of modern computing.. Riga : University of Latvia. 2017, vol. 5, iss. 2, p. 183-205.. ISSN 2255-8942. eISSN 2255-8950 |
| Keywords [eng] |
earning object ; generative learning object ; content generation and adaptation ; stage-based model |
| Abstract [eng] |
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. |
| Published |
Riga : University of Latvia |
| Type |
Journal article |
| Language |
English |
| Publication date |
2017 |
