This study's goal was to determine if appropriately designed, completely virtual formats of language courses are able to engender in students speaking ability progress comparable to that fostered within traditional face‐to‐face (F2F) classrooms. The study further sought to clarify language learners' perceptions of online Japanese courses. The speaking ability of all participants was assessed at the beginning and end of their Japanese language course. A mixed‐design analysis of variance was conducted to analyze the effect of course type on speaking ability. Results showed that participant speaking ability improved significantly over time regardless of course type (online or F2F). This study also revealed that the majority of participants were able to use online tools with ease and found the online format to be an effective method of learning Japanese. Two principal reasons for studying Japanese online are reported. Implications and suggestions are discussed.
Abstract. Recent research has demonstrated that learners of Japanese struggle producing correct Japanese pitch accent. The goal of the present study was to investigate the effect learners' first language (L1) may have on accent acquisition following the introduction and use of a digital Japanese Pitch Accent Learning and Practice (PALP) program in two Japanese courses. The PALP program visually and aurally presents learners with pitch patterns and requires them to select the correct pitch accent pattern for new vocabulary. Participants' pitch accent abilities were assessed at the beginning and end of their courses. A mixed design ANOVA was conducted to analyze the effect of learners' L1 on pitch accent acquisition. Results evince a significant interaction effect between participant group (treatment/control) and L1 (Chinese/English), F(1, 24) = 10.09, p < .01 ( 2 = .30). Specifically, English L1 participants in the treatment group considerably outperformed the control group English L1 participants. However, the Chinese L1 participants in both groups performed at approximately equal levels. These results suggest the existence of an L1 influence on pitch accent acquisition.
The last ve years have been a period of exponential growth in the number of machines connected t o the Internet and the speed at which these machines communicate. The infrastructure is now in place t o c onsider a nationwide cluster of workstations as a viable parallel processing platform. In order to achieve acceptable performance on this kind of a machine, performance p r e diction tools must provide information on where to place c omputational objects. Incorrect object placement can result in poor performance and congestion in the network. This research develops a new paradigm for predicting performance in the Wide Area Network (WAN) based cluster arena. Statistical samples of the performance of clusters and applications are used to build characteristic surfaces. These surfaces are then used t o p r ovide guidance in placement of new applications. This prediction method i s intended to minimize both the execution time of the application and the impact of the application on the nationwide virtual machine. Performance p r e diction tools are a n i m p ortant prerequisite to eectively utilizing WAN based clusters.
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