A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The Bi-LSTM is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the verbal features and keywords, and the generated keywords were weighed in accordance with the attention mechanism. A total of 12,000 sentences generated in online collaborative discussion activities have been classified into six categories, namely statement, negotiation, question, management, emotion and others. Results showed that the classification accuracy of Attention-Bi-LSTM reached 81.50%, which is higher than that of the baseline Bi-LSTM model. This study theoretically uncovers the features of collaborative discussion of onliners and practically provides an effective approach to automatic behaviour analysis in an online context.

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

Collaborative problem solving (CPS) is an influential human behavior affecting working performance and well-being. Previous studies examined CPS behavior from the perspective of either social or cognitive dimensions, which leave a research gap from the interactive perspective. In addition, the traditional sequence analysis method failed to combine time sequences and sub-problem sequences together while analyzing behavioral patterns in CPS. This study proposes a developed schema for the multidimensional analysis of CPS. A combination sequential analysis approach that comprises time sequences and sub-problem sequences is also employed to explore CPS patterns. A total of 191 students were recruited and randomly grouped into 38 teams (four to six students per team) in the online collaborative discussion activity. Their discussion transcripts were coded while they conducted CPS, followed by the assessment of high- and low- performance groups according to the developed schema and sequential analysis. With the help of the new analysis method, the findings indicate that a deep exploratory discussion is generated from conflicting viewpoints, which promotes improved problem-solving outcomes and perceptions. In addition, evidence-based rationalization can motivate collaborative behavior effectively. The results demonstrated the potential power of automatic sequential analysis with multidimensional behavior and its ability to provide quantitative descriptions of group interactions in the investigated threaded discussions.