Little research has been done on the effect of learning context on L2 listening development. Motivated by DeKeyser’s (2015) skill acquisition theory of second language acquisition, this study compares L2 listening development in study abroad (SA) and at home (AH) contexts from both language knowledge and processing perspectives. One hundred forty-nine Chinese postgraduates studying in either China or the United Kingdom participated in a battery of listening tasks at the beginning and at the end of an academic year. These tasks measure auditory vocabulary knowledge and listening processing efficiency (i.e., accuracy, speed, and stability of processing) in word recognition, grammatical processing, and semantic analysis. Results show that, provided equal starting levels, the SA learners made more progress than the AH learners in speed of processing across the language processing tasks, with less clear results for vocabulary acquisition. Studying abroad may be an effective intervention for L2 learning, especially in terms of processing speed.

The present study aimed to investigate second language (L2) word-level and sentence-level automatic processing among English as a foreign language students through a comparative analysis of students with different proficiency levels. As a multidimensional and dynamic construct, automaticity is conceptualized as processing speed, stability, and accuracy which are indexed by reaction time, coefficient variation and accuracy rate. Sixty students (39 undergraduate students and 21 graduate students) who majored in English participated in this study. They completed the lexical semantic classification task, the sentence construction task, the sentence verification task under two different modalities (visually- and aurally-presented situations). Multivariate analyses were conducted to examine the differences between the students with different proficiency levels pertaining to their L2 performance. The results indicated that the processing speed was not found to be a good indicator of automatic language processing. Moreover, both levels of students appeared to reach a plateau in word-level processing but there were some variations in students' sentential processing. Finally, the findings showed that automatic language processing seemed to be module-specific and non-sharable across different modalities and skills.

Data-driven modeling has attracted wide attention in academia because of its effectiveness. However, Due to the lack of data, some traditional modeling methods, such as extreme learning machine (ELM), can’t achieve high learning accuracy. A novel approach based on Mega-Trend-Diffusion (MTD) and Monte Carlo is presented in this paper to deal with the problem, named Monte Carlo Mega-Trend-Diffusion (MCMTD). The proposed approach utilizes MTD to estimate the acceptable range of the attributions and Latin hypercube sampling method to sample. ELM is employed to establish the prediction model. In this paper, two real data sets, the multi-layer ceramic capacitors (MLCC) and the purified terephthalic acid (PTA), are used to verify the effectiveness and reasonability of MCMTD. The experimental results show that MCMTD can significantly enhance the accuracy and ability of the forecasting model.

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