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Background: Social network awareness (SNA) enables students to know the online learning context of peers in computer-supported collaborative learning (CSCL) and it has been used to improve peer interaction and participation in collaborative learning tasks. Mobile learning enables students to access learning content and discuss content with peers regardless of location or time. Because almost every college student has their own mobile device and social networks are ubiquitous in their daily lives, a mobile learning application with social networking is promising for promoting effective learning if students are willing to continually use the application. However, few studies have investigated this. Objectives: In this study, SNA was integrated into a mobile-based assessment (MBA-SNA). The study investigated whether students were willing to persist in the use of MBASNA and the factors influencing this intention. Methods: A 1-month experiment was conducted to investigate the research question on the basis of the unified theory of acceptance and use of technology (UTAUT). Results: The results demonstrated that (1) students were willing to continue using the MBA-SNA, (2) the incentives influencing behavioural intention (BI) in initial use and continual use differed, and (3) social influence (SI) and perceived playfulness (PP) were the two key determinants of continual use. Implications: The SNA increases the positive impact of SI and PP on MBA continual use so that continual use can be assured. Future research should integrate SNA into other mobile learning apps (e.g., mobile collaborative writing or discussion apps) to enhance continued system use and learning effectiveness.

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally, self-regulation behavior significantly influences online learning of students. However, exactly how network centrality and self-regulation influence learning behavior and effectiveness in an e-learning environment remains unclear. Therefore, this study investigates how both variables (ie, network centrality and self-regulation) impact student learning in an SNA-related e-learning environment. Analytical results indicate that the student group with high-level centrality and low-level self-regulation more significantly progresses in learning achievement than the other groups. The second finding shows the group also has the highest number of students asking for help, revealing they have the highest system utilization rate.

This study explores a de-bias function for a decision support systems (DSS) that is designed to help a user avoid confirmation bias by increasing the user's learning opportunities. Grounded upon the theory of mental models, the use of DSS is viewed as involving a learning process, whereby a user is directed to build mental models so as to reduce the tendency to consider only data that supports any preexisting mental models. The results of an experiment suggest that a de-bias function, called computer-mediated counter-argument (CMCA), facilitates individual learning and improves decision satisfaction. (Contains 7 tables and 2 figures.)

This study explores a de-bias function for a decision support systems (DSS) that is designed to help a user avoid confirmation bias by increasing the user's learning opportunities. Grounded upon the theory of mental models, the use of DSS is viewed as involving a learning process, whereby a user is directed to build mental models so as to reduce the tendency to consider only data that supports any preexisting mental models. The results of an experiment suggest that a de-bias function, called computer-mediated counter-argument (CMCA), facilitates individual learning and improves decision satisfaction. [ABSTRACT FROM AUTHOR]

With the growth of big data and open data in recent years, the importance of data anonymization is increasing. Original data need to be anonymized to prevent personal identification from being revealed before being released to the public. There is a growing variety of de-identification methods which have been proposed to reduce the privacy issues, however, there is still much to be improved. The purpose of this study is to demonstrate the possibilities of re-identification from masked data, and to compare the pros and cons of different de-identification methods. A set of electronic toll collection data from Taiwan was used and we successfully re-identified vehicles with specific patterns. Four de-identification methods were performed and finally we compared the strengths and weaknesses of these methods and evaluated their appropriateness. [ABSTRACT FROM AUTHOR]