Abstract
Wikis are attracting lots of attention for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Recommendation systems (RS) can provide useful content recommendations in different contexts. To our best knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning in wikis. Therefore, we propose a personalized content recommendation framework to extrapolate topical navigation graphs from learners' free navigation and integrate them with fuzzy thesauri for automatic and adaptive personalized content recommendations to support informal learning in wikis.
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