Anxiety plays an influential role in foreign language learning. However, a lack of attention was paid to examining the effects of anxiety levels on learning performance and gaming performance in digital game-based learning. To this end, this study developed a game-based English learning system and investigated how different levels of anxiety affected learners' learning performance and gaming performance. A quasi-experiment was conducted in an elementary school. The results showed that high-anxiety learners performed worse than low-anxiety learners in speaking, word/ sentence match, and overall learning performance. However, they performed similarly in listening performance. Moreover, the results showed that high-and low-anxiety learners demonstrated a similar level of gaming performance. A subsequent analysis showed that significant correlations existed between learning performance and gaming performance for learners with high anxiety whereas such positive correlations were rarely found for learners with low anxiety, indicating that high-anxiety learners' learning performance could be fostered by their gaming performance. The findings suggested that digital game-based learning was particularly beneficial to high-anxiety learners, whose gaming performance was a facilitative factor of their learning performance.

This paper proposes a low-cost field-programmable gate-array (FPGA)-based brain-computer interface (BCI) multimedia control system, different from the BCI system, which uses bulky and expensive electroencephalography (EEG) measurement equipment, personal computer, and commercial real-time signal-processing software. The proposed system combines a customized stimulation panel, a brainwave-acquisition circuit, and an FPGA-based real-time signal processor and allows users to use their brainwave to communicate with or control multimedia devices by themselves. This study also designs a light-emitting diode stimulation panel instead of cathode ray tube or liquid-crystal display used in existing studies, to induce a stronger steady-state visual evoked potential (SSVEP), a kind of EEG, used as the input signal of the proposed BCI system. Implementing a prototype of the SSVEP-based BCI multimedia control system verifies the effectiveness of the proposed system. Experimental results show that the subjects' SSVEP can successfully control the multimedia device through the proposed BCI system with high identification accuracy.

A novel Kalman filter-based adaptive observer for the sampled-data nonlinear time-varying system is proposed in this paper. With the high gain property of Kalman filter, it is applicable to a large variation of unknown parameters, which can be estimated optimally. Then a method of actuator fault detection is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. Additionally, the optimal linearization technique is used to obtain the locally optimal linear model for a nonlinear system at each sampled state, so that the actuator fault detection and performance recovery of a sampled-data nonlinear time-varying system is accomplished. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller.

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