Knowledge Based Recommender System and Web 2.0 to Enhance Learning Model in Junior High School

Current educational trend is education based on student-centered activities, personalize and collaborative. Along with the development of technology, blended learning becomes very popular in education. A good blended learning is a blended learning that fits the current educational challenges of combining learning activities, supported by tools and technologies such as web 2.0 tools and recommender systems. In previous research, has been built a recommender system that recommends e-tivity, possible collaborators, web 2.0 tools, and bits of advice. The recommender system was built with collaborative filtering and content-based techniques. Collaborative filtering and content-based have problems in terms of dependency with data history, so it is possible to ramp-up and gray sheep on the recommendation. One way to overcome the problem of ramp-up and p-ay sheep is with knowledge-based. This paper proposes a knowledge-based recommender system to improve learning model in schools. Recommendations are based on VAK learning styles and collaborative learning theory. This research uses Design Research Methodology. The results showed that the experimental class score was higher than the control class. Using inferential statistics, it can be concluded that proposed knowledge based recommender system significantly enhance learning in school.