Experiential activities are the most direct form of ecological teaching, so outdoor education is an important way of learning. Through direct observation and life experience, students will learn about the ecological environment, understand the importance of the ecological environment, raise their awareness of environmental protection, and put into practice the protection of the ecological environment. This study involved plant teaching activities that incorporated school plant learning paths into environmental education, so that students could learn about flora, experience nature, and take care of the environment. We created an augmented reality application for use in schools that takes the user on an ecological tour of the plants in the diet of butterflies. The application formed the localized and special mobile learning content in a school. Students were allowed to walk out of the classroom and use their mobile device to engage in autonomous learning, and we then determined their acceptance of the augmented reality application. Through this application we were able to tell whether students were able to adapt to learning with technology combined with traditional teaching methods. This application was tested on a group of elementary school students, who were then interviewed using quantitative and qualitative research methods to understand the users’ feedback.

The novel coronavirus (COVID-19) pandemic is rampant around the world, and teachers and students are unable to attend physical classes in the midst of a serious outbreak. This study aims to design a user-friendly, educational chatbot application interface that can be used as an after-school self-learning tool for students to enhance their interest and comprehension and increase the effectiveness of their learning at home. The system adopts the Chatfuel platform as the core interface and incorporates augmented reality technology to build a chatbot that allows users to interact with it after they have logged in to Facebook. The content is based on the biology subject of the first year of junior high school and is integrated into the online teaching with augmented reality teaching materials. A user survey is conducted to understand students’ attitudes towards learning biology with the aid of the ARCS motivation model, with 102 valid questionnaires received. The results show that the AR-based chatbot system developed in this study significant influenced the indicators in the ARCS motivation model; therefore, the intention to use the system is presumed to result in a noticeable increase in student learning outcomes when using the system. Accordingly, this study proposes new online learning tools for students to use at home during the pandemic, and the system also provides references for the future development and modification of educational chatbots.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students’ professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact-, peer-, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, “Funny Monkey”, is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

Path planning is a key problem in the design of autonomous driving systems, and accurate traffic light detection is very important for robust routing. In this paper, we devise an object detection model, which mainly focuses on traffic light classification at a distance. In the past, most techniques employed in this field were dominated by high-intensity convolutional neural networks (CNN), and many advances have been achieved. However, the size of traffic lights may be small, and how to detect them accurately still deserves further study. In the object detection domain, the scheme of feature fusion and transformer-based methods have obtained good performance, showing their excellent feature extraction capability. Given this, we propose an object detection model combining both the pyramidal feature fusion and self-attention mechanism. Specifically, we use the backbone of the mainstream one-stage object detection model consisting of a parallel residual bi-fusion (PRB) feature pyramid network and attention modules, coupling with architectural tuning and optimizer selection. Our network architecture and module design aim to effectively derive useful features aimed at detecting small objects. Experimental results reveal that the proposed method exhibits noticeable improvement in many performance indicators: precision, recall, F1 score, and mAP, compared to the vanilla models. In consequence, the proposed method obtains good results in traffic light detection.

We first describe how several existing software reliability growth models based on nonhomogeneous Poisson processes (NHPPs) can be derived based on a unified theory for NHPP models. Under this general framework, we can verify existing NHPP models and derive new NHPP models. The approach covers a number of known models under different conditions. Based on these approaches, we show a method of estimating and computing software reliability growth during the operational phase. We can use this method to describe the transitions from the testing phase to operational phase. That is, we propose a method of predicting the fault detection rate to reflect changes in the user's operational environments. The proposed method offers a quantitative analysis on software failure behavior in field operation and provides useful feedback information to the development process

With recent strong emphasis on rapid development of information technology, the decisions made on the basis of early software reliability estimation can have greatest impact on schedules and cost of software projects. Software reliability prediction models is very helpful for developers and testers to know the phase in which corrective action need to be performed in order to achieve target reliability estimate. In this paper, an SVM-based model for software reliability forecasting is proposed. It is also demonstrated that only recent failure data is enough for model training. Two types of model input data selection in the literature are employed to illustrate the performances of various prediction models.