With the increasing level of internationalization in higher education, the number of international students in mainland China is rapidly increasing. However, limited research has considered that student results may be affected by a reduced motivation to learn. Therefore, the aim of this research is to explore the effect of the learning motivation on the learning outcomes of international students and the moderating role of learning experience. A sample of 130 international students from 23 countries studying in mainland China was analyzed. (...) The study found a significant correlation between the learning motivations and international students' learning outcomes. It was also determined that learning experience has significantly enhances the relationship between learning motivation and the learning outcomes of international students. This study contributes to the higher education literature of on learning motivation by students and learning outcomes. (shrink)

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of (...) three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks. (shrink)