Abstract
Information wikis and especially Wikipedia have become one of the most attractive environments for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently con-struct knowledge without being required to follow a predefined learning path in line with the constructivist learning theory. Link-based navigation and keyword-based search methods used on Wikipedia and similar information wikis suffer from many limitations. In our paper, we present an effective recommendation system that provides easier and faster access to relevant content on Wikipedia to support informal learning. In addition, we evaluate the impact of personalized content recommendations on informal learning from Wikipedia and show how web analytics data can be used to get an in-sight on informal learning in similar environments.
Highlights
Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences [1]
In this paper, we present a personalized content recommendation framework for information wikis in addition to an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis
In our evaluation we use two types of metrics: user-centric quality metrics to evaluate the effectiveness of the personalized recommendations; and objective educational metrics and web analytics data to evaluate the impact of recommendations on learning
Summary
Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences [1]. The availability/unavailability and adequacy/inadequacy of benchmark datasets The number of users that such evaluations may require In addition to these factors, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. The introduced recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users It integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. We evaluate the impact of personalized recommendations on informal learning by assessing conceptual knowledge in users’ feedback.
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