Technological impact on language anxiety dynamic Abstract   Insufficient information was generated from the existing literature of crosssectional studies about the changes of language learning anxiety, especially in technologyassisted language settings. This paper filled the gap by designing a longitudinal study of 10 weeks in which the mobile learning apps of Rain Classroom was administered to 158 postgraduate students in language class. Quantitative results were generated, using paired samples Ttest and oneway repeated measures ANOVA, from the 5point Likert scale of English Language Class Anxiety Scale, 7point scale recall of anxiety changes across 4 weeks, pre and posttest language performance, combined with the qualitative interview transcripts administered before and after the learning process. In consistency with prior findings, the study results produced a significant decrease in anxiety in general, corroborating the interview and the selfrecalling measure results. Further, the selfrecalled scale revealed a more complex pattern of anxiety with the general decreasing tendency mixed with the increasing trend between the last two weeks. This study implied that the binary approach of anxiety reduction was not sufficient for the big picture of fluctuations and variations of language anxiety. The combination of language outcome data reinforced the explanatory forces than a singledimensional dataset.

Development of Mobile Application for Word Reordering Problems: An Attempt Oriented to Learning Analytics The authors have developed a mobile application for Word Reordering Problems (WRPs) which was designed to collect learnersu0027 study logs, such as finger trajectories, to calculate parameter values used in detecting hesitation of learners. Additionally, the authors also have developed a simple Learning Management System (LMS) to store learnersu0027 study logs and visualize various parameters or data charts generated by the logs. By comparing the study logs collected on the WRP mobile application with its web version, it is discussed whether or not the parameters provided by the mobile application could also be used to locate the hesitation correctly as previously researched using web application with supervised learning. Another objective of this research is to exploit the practical use of new data types provided by physical sensors embedded in mobile devices. A pilot study showed how the accelerometer values are influenced in the event of accidents such as a sudden stop of a train. By analyzing multiple new data types, we attempt to acquire more information in the process of solving WRPs on mobile devices.