In this paper, we discuss the teaching effects of augmented reality (AR) technology in German instruction. We conducted one prestudy and three formal studies on German learners in China’s mainland and Taiwan region. In the formal studies, a total of 120 students participated in the survey, allowing us to compare the differences in interest in learning between AR picture books and traditional picture books. A total of 114 students took part in the survey, which enabled us to compare the contribution of AR picture books to teaching when students’ satisfaction and German proficiency were different. To improve satisfaction, 514 students participated in the survey regarding the influence of the interactive narrative design effect and peer learning on satisfaction with using AR picture books. The results suggest that when learning German with AR picture books, satisfaction is the key construct that determines students’ learning states.

In terms of the teaching process of matte painting, it is essential for students to develop a sound understanding of the relationship between virtual and physical environments. In this study, first-person view (FPV) drones are applied to matte painting courses to evaluate the effectiveness of the teaching, and to propose more effective design suggestions for FPV drones that are more suitable for teaching. This provides students with a better learning environment using a digital education system. The results of the study indicate that the flow experience, learning interest, and continuous learning intention of students who use FPV drones in matte painting are significantly greater than those of students who only utilize traditional teaching methods. Furthermore, the technology incentive model (TIM) was developed in this study after being verified by the structural equation model. The results demonstrate that the second-order construct ‘technology incentive’ comprising perceived interactivity, perceived vividness, and novel experience positively influence students’ learning interest and continuous learning intentions under the mediation of flow experience.

We propose an image quality assessment metric based on quaternion singular value decomposition that represents a color image as a quaternion matrix, separates image noise information using singular value decomposition and extracts features from both the whole image and its noise information. In the proposed method, the color image and its local variance are represented by using quaternion and then performing singular value decomposition. Later, 75% of singular values are taken as image noise information. We extract a luminance comparison, contrast comparison, structure comparison, phase congruency and gradient magnitude from whole color images and extract the peak signal-to-noise ratio from image noise information as features. Finally, these features are used as the input to a kernel extreme learning machine to predict the quality of the tested images. Extensive experiments performed on four benchmark image quality assessment databases demonstrate that the proposed metric achieves high consistency with the subjective evaluations and outperforms state-of-the-art image quality assessment metrics. INDEX TERMS Quaternion singular value decomposition, kernel extreme learning machine, image quality assessment.

IntroductionThis study examines the emotional support offered by the non-player characters (NPCs) in an interactive learning environment, as well as the effects of the perceived playfulness of the interactive system on German language learning.MethodWe developed a role-playing library system to serve this purpose. 2,377 Chinese Internet users were surveyed using online questionnaire.ResultsA theoretical model of emotion- driven learning (ELM) was proposed based on the analysis results of valid recovered data. Additionally, NPCs were found to be effective in improving learning outcomes through emotional support.DiscussionAn interactive education system may be able to enhance the perceived playfulness of learning in order to enhance the learning experience.

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