Development of on-line courses focusing on quality

The purpose of this paper is to show by which means quality in on-line education is achieved at Dalarna University. As a leading provider of online university courses in northern Europe, both in terms of number of students conducting their studies entirely on-line compared to the whole student body, (approximately 70% on-line students all subjects included), Dalarna University has acquired de facto extensive practical experience in the field of information technologies related to distance education. It has been deemed essential, to ensure that the quality of teaching reflects the principles governing the assessment of learning so that on-line education is deemed as comparative to campus education, both from a legal and cognitive point-of-view. Dalarna University began on-line courses in 2002 and it soon became clear that the interaction between the teacher and the student should make its mark in all stages of the learning process in order to both maintain the learners' motivation and ensure the assimilation of knowledge. We will illustrate these aspects by giving examples of what has been done in the recent years in on-line teaching of languages. As this method of teaching is not limited to learning basic language skills, but also to the study of literature, social issues and the language system of the various cultures, our presentation will offer a broad range of areas where the principles of quality in education are provided on a daily basis.

Collaborative Filtering with Data Classification : A Combined Approach to Hotel Recommendation Systems

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.