A Comparison of MCQ and AGLO Generative Learning Object Models

Ciprian-Bogdan Chirila

University Politehnica of Timisoara, Romania,
Faculty of Automation and Computer Science
E-mail: chirila(at)cs.upt.ro

Abstract

Generative learning objects are the second generation of learning objects representing instantiable patterns designed for reuse purposes. Among them we identified in the literature two concrete models: Moodle Coordinate Questions and Auto-generative Learning Objects. Each model has its own approach of creation and generation of student consumable learning objects. A comparison and an analysis of the semantic details will help us to improve both models.

Keywords

learning objects, generative learning objects formats, variables, random values

BibTex

@Article{Chirila2015FIHA,
	author			=		{Ciprian-Bogdan Chirila},
	title			=		{A Comparison of MCQ and AGLO Generative Learning Object Models},
	journal			=		{Annals of Faculty of Engineering Hunedoara},
	pages			=		{1--6},
	volume			=		{4},
	isbn			=		{},
	address			=		{Hunedoara, Romania},
	month			=		{November},
	year			=		{2015},
}

Text

[Chirila2015FIHA] - Ciprian-Bogdan Chirila. A Comparison of MCQ and AGLO Generative Learning Object Models, Annals of Faculty of Engineering Hunedoara, Hunedoara, Romania, November, 2015.

Data fields

[Chirila2015FIHA] - 
Ciprian-Bogdan Chirila.
A Comparison of MCQ and AGLO Generative Learning Object Models,
Annals of Faculty of Engineering Hunedoara,
Hunedoara, Romania,
November,
2015.