(untitled) (International Conference on Computers in Education, 2016)
Learning English on Internet has become increasingly common, where Internet movies for learning English are also booming. For instance, the VoiceTube, an Internet video platform for learning English, has pluralistic free videos with both Chinese and English captions. Meanwhile, VoiceTube can combine social media to create a learning network community. However, a good recommendation system is necessary to select proper videos from the English film resources according personal preferences. Hence, the present study used collaborative filtering method to recommend videos, which were found in the learners’ lists with similar preferences. First, we used a web crawler to crawl user information on VoiceTube. Then, the Crab, which is recommender engine in Python, was used to analyze the collected data for identifying similar learners. According to the preference scores, Crab can precisely recommend proper English learning films to every learner. Finally, we created a query interface for the data crawled from VoiceTube. Thus, learners can use the query function to search friends, collecting similar favorite movies, through VoiceTube social networks. As a result, learners can passively get recommended videos or actively select proper English movies that can enhance their motivation of learning English through watching videos.