The social media usage has penetrated to the many areas in daily lives of today's students. Therefore, social media can be effective tool to support their educational communications and collaborations with their friends and also faculty members. This study aims to determine the effects of social media on collaborative learning. For this purpose, a theoretical model is proposed based on comprehensive literature review. Using an online questionnaire, data are collected from the students of one of the largest university in Turkey. Structural equation modelling is employed as the major statistical analytic technique. The theoretical model is supported by the findings significantly. The findings indicate that perceived ease of use is a predictor of perceived usefulness and both of these have impact on social media use of students for educational purposes. Social media usage improves peer interaction and course engagement of students and also students' interaction with faculty members. Finally, peer interaction and course engagement have positive significant effect on collaborative learning. The results of the study might be helpful to students and educational leaders in their efforts to create initiatives to support, promote, and encourage the implementation and usage of social media in blended learning classes and provide adequate training for teachers to increase social media adoption.

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It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

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The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. When used separately, each existing venue recommendation system algorithm has its own drawbacks (e.g. cold start, data sparsity, scalability). Another issue is that critical information about context is not commonly used in venue recommendation systems. This article proposes a hybrid recommendation model that combines contextual information, user-based and item-based collaborative filtering and content-based filtering. For this purpose, we collected user visit histories, venue-related information (distance, category, popularity and price) and contextual information (weather, season, date and time of visits) related to individual user visits from Twitter, Foursquare and Weather Underground. Experimental evaluation of the proposed hybrid system (HybRecSys) using a real-world dataset shows better results than baseline approaches.