Learning a second language (L2) presents a significant challenge to many people in adulthood. Platforms for effective L2 instruction have been developed in both academia and the industry. While real-life (RL) immersion is often lauded as a particularly effective L2 learning platform, little is known about the features of immersive contexts that contribute to the L2 learning process. Immersive virtual reality (iVR) offers a flexible platform to simulate an RL immersive learning situation, while allowing the researcher to have tight experimental control for stimulus delivery and learner interaction with the environment. Using a mixed counterbalanced design, the current study examines individual differences in L2 performance during learning of 60 Mandarin Chinese words across two learning sessions, with each participant learning 30 words in iVR and 30 words via word–word (WW) paired association. Behavioral performance was collected immediately after L2 learning via an alternative forced-choice recognition task. Our results indicate a main effect of L2 learning context, such that accuracy on trials learned via iVR was significantly higher as compared to trials learned in the WW condition. These effects are reflected especially in the differential effects of learning contexts, in that less successful learners show a significant benefit of iVR instruction as compared to WW, whereas successful learners do not show a significant benefit of either learning condition. Our findings have broad implications for L2 education, particularly for those who struggle in learning an L2.

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Congestion control is a very important problem which can affect network performance directly. However, owing to the lack of steady end-to-end connection and high latency, the traditional congestion control mechanism based on end-to-end feedback is not feasible in DTN. Because obtaining the global information of network is difficult, the congestion control decisions should be made autonomously with local information only. We propose a novel distributed congestion control algorithm based on epidemic routing protocols----MACRE (Message Admission Control based on Rate Estimation). Preliminary experimental results show that this congestion control mechanism can improve the network performance efficiently. InstructionDTN is a new kind of network which has bright characteristics: high delay and low data rate, no steady existence of link between end to end, lacking of interaction in the network architecture, the system resources and the life time is limited and so on. Therefore, this kind network also be called as challenged network [1] . In order to overcome the restrictions mentioned above, sustain all kinds of applications in DTN better, DTN adopts "storage-carry-forward" routing [2] to take place of traditional "storage-forward" routing. Because of the instability of the end-to-end path, the replication of messages is a normal method to ensure the delivery ratio of messages. Typical routing protocol such as epidemic routing protocol [3] can improve the delivery ratio by increasing the amount of repetitions of messages and the forward opportunity of messages. However this kind of mechanism will bring large number of repetitions, and even when the messages have been successfully received by the destination, a lot of redundant repetitions will be stored in the network yet. Then the limited resources of network will be exhausted rapidly by the gigantic amount of repetitions, which will result in network congestion and decline of the delivery ratio finally. Consequently, how to avoid and control network congestion efficiently has became a key problem to improve the performance of network。 Related WorksCongestion control is a new research field in DTN, compared with that of traditional network, congestion control in DTN is more difficult. The lack of steady end-to-end path and the dynamic characteristics of DTN make the traditional congestion control policy based on feedback in TCP/IP is not applicable in delay/interrupt tolerant environment.Congestion control mechanism can be divided into Proactive Policy and Reactive Policy [4] . Proactive Policy usually applies admission control to avoid network congestion at the beggining. Scott Burleigh presents a local autonomy congestion control mechanism based on rules [5] . This mechanism adopts economics model and compares the receipt and forwarding of messages to risk investment. When a new message arrives, node independently decides whehter to receive it or not according to a risk value of receiving and storing the message instead of the end-to-end feedback