A competence-based system for recommending study materials from the Web

Abstract

Adaptive hypermedia systems, such as intelligent tutoring systems, aim to reduce reliance upon a teacher. However, such systems have some drawbacks such as inconsistency when estimating a learner's knowledge level, and a lack of a pedagogically informed approach to teaching and learning. These drawbacks may be addressed by a competency model. Such a model has the benefits of an improved pedagogical approach to e-learning and a more consistent profile of learners' competences. Such a model also renders competences machine processable, sharable, and modifiable.

The aim of this research is to investigate and design a competence-based system which provides appropriate study materials from the Web to the learner without any intervention from the teacher. Each step within the system for deriving the study material links from the learners' competences is described in detail.

A competence structure is designed from a set of intended learning outcomes. An XML-schema represents the information within a competence structure to support machine processing.

Experiments were carried out to evaluate the competence-based system for recommending links by considering the learner's reaction, by comparing the learning improvement between the competence-based approach and other approaches, and by exploring the effects of search engines used and keywords on the search results.

From these experiments, some conclusions have been drawn, such as: learning paths with more nodes are more helpful, and Web links of a competence node with a lower level of Bloom's taxonomy showed higher ratings than those with a higher level of Bloom's taxonomy. In addition, a competence-based system is accepted by learners at the reaction level. A freely-browsing and a competence-based system produced equal improvements in learners' learning. Different types of search engines (Google and Google API) and categories of keywords (SM and CA+SM+CO) show no significant differences between the qualities of study material links in helping learners achieve their competences. Furthermore, the links from Google were found to be as good as those from an educational search engine.

Some future work is suggested, for example, more exploration of a complex competence structure and learning paths, improvements on the usability and accessibility of the application, and more in-depth consideration of self-assessment.

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Identifiers

ORCID for Gary Wills: orcid.org/0000-0001-5771-4088

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