Evaluating the Impact of Personalized Content Recommendations on Informal Learning from Wikipedia

摘要

Information wikis and especially Wikipedia are attracting an increasing 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. To the best of our knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning from wikis. Therefore, we propose a personalized content recommendation framework that extrapolates topical navigation graphs from learners' free navigation and integrates them with fuzzy thesauri for automatic and adaptive personalized content recommendations to support informal learning in wikis. We design user studies and conceptual knowledge rubric to evaluate the impact of personalized recommendations on learning from Wikipedia. Results show that the proposed personalized content recommendation framework generates highly relevant recommendations. Evaluation of informal learning reveals that users who use Wikipedia with personalized recommendations can achieve higher scores on conceptual knowledge assessment compared to those who use Wikipedia without recommendations. Learners who use Wikipedia with personalized recommendations are able to utilize larger number of concepts and are able to make comparisons and state relations between concepts.