Massive Open Online Courses (MOOCs)

The games industry has been growing prosperously with the development of information technology. Recently, with further advances in social networks and mobile services, playing mobile social gaming has gradually changed our daily life in terms of social connection and leisure time spending. What are the determinant factors which affect users intention to play such games? Therefore in this research we present an empirical study on WeChat, China’s most popular mobile social network, and apply a technology acceptance model (TAM) to study the reasons beneath the popularity of games in mobile social networks. Furthermore, factors from social and mobile perspective are incorporated into the conventional TAM and their influence and relationships are studied. Experimental study on accumulated online survey data reveals several interesting findings and it is believed that this research offers the researchers in the community further insight in analysing the current popularity and future potential of mobile social games.

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Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences. This paper introduces a learning style model to represent features of online learners. It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style (AROLS), which implements learning resource adaptation by mining learners' behavioral data. First, AROLS creates learner clusters according to their online learning styles. Second, it applies Collaborative Filtering (CF) and association rule mining to extract the preferences and behavioral patterns of each cluster. Finally, it generates a personalized recommendation set of variable size. A real-world dataset is employed for some experiments. Results show that our online learning style model is conducive to the learners' data mining, and AROLS evidently outperforms the traditional CF method.

Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.