Due to the emergence of computer education, AI education, the Internet of Things, big data, and technological wisdom, it is easy for students to be distracted when engaged in traditional education. Flipped teaching is a teaching strategy frequently used in colleges and universities. The focus of this research was conducted by a comparative analysis of the cognitive load between the experimental group and the control group through a quasi-experimental design for research with different learning methods and different classes. More specifically, flipped teaching was carried out with an experimental group, and traditional teaching a control group; they were observed at the same time, and 213 private university students participated in the experiment. The research proposes a practice of mixed teaching, carried out in a group communication behavior system, and enhancing the spirit of group interaction and learning through mobile devices. The core value of the research lies in (1) online learning, (2) group interaction, and (3) the learning load of the conceptual model. In addition, focus group interviews were used to provide feedback on participants’ cognition and emotions. The results indicate that there were differences in cognitive load between the two classes.

The global population is gradually entering an aging society; chronic diseases and functional disabilities have increased, thereby increasing the number of people with limitations. Therefore, the demand for assistive devices has increased substantially. Due to numerous and complex types of assistive devices, an assessment by a professional therapist is required to help the individual find a suitable assistive device. According to actual site data, the assessment needs of “wheelchairs” accounted for most of the cases. Therefore, this study identified five key evaluation characteristics (head condition, age, pelvic condition, cognitive ability, and judgment) for “transit wheelchairs” and “reclining and tilting wheelchairs” from the diagnostic records of “wheelchairs” using the classification and regression trees (CART) decision tree algorithm. Furthermore, the study established an evaluation model through the Naïve Bayes classification method and obtained an accuracy rate of 72.0% after a 10-fold cross-validation. Finally, the study considered users’ convenience and combined it with a LINE BOT to allow the user or the user’s family to engage in self-evaluation. Preliminary suggestions for wheelchair types were given through the assessment model so that evaluators could not only determine a case’s situation in advance and reduce the time required for fixed-point or home assessments, but also help cases find the appropriate wheelchair type more easily and quickly.

This paper presents the TTIPP methodology, an integration of task analysis, task ontology, integration definition function modeling (IDEF0), Petri net, and Petri net mark language (PNML), to organize and model the task knowledge in the form of natural language expressions acquired during the knowledge-acquisition process. The goal of the methodology is to make the tasks more useful, accessible, and sharable through the web for a variety of stakeholders interested in solving a problem which is expressed mostly in linguistic form, and to shed light on the nature of problem-solving knowledge. This study provides a core epistemology for the knowledge engineer while developing the task ontology for a generic task. The proposed model overcomes the drawbacks of IDEF0, which are its static nature and Petri net which has no concept of hierarchy. A good number of countries lie on the typhoon and earthquake belts, which make them vulnerable to natural calamities. However, a practical incident command system (ICS) that provides a common framework to allow emergency responders of different backgrounds to work together effectively for standardized, on-the-scene, incident management has yet to be developed. There is a strong need to explicitly share, copy, and reuse the existing problem-solving knowledge in a complex ICS. As an example, the TTIPP model is applied to the task of emergency response for debris-flow during a typhoon as a part of an ICS.

The hierarchical-based structure is recognized as a favorable structure for wireless local area network (WLAN) positioning. It is comprised of two positioning phases: the coarse localization phase and the fine localization phase. In the coarse localization phase, the users’ positions are firstly narrowed down to smaller regions or clusters. Then, a fingerprint matching algorithm is adopted to estimate the users’ positions within the clusters during the fine localization phase. Currently the clustering strategies in the coarse localization phase can be divided into received signal strength (RSS) clustering and 3D clustering. And the commonly seen positioning algorithms in the fine localization phase include k nearest neighbors (kNN), kernel based and compressive sensing-based. This paper proposed an improved WLAN positioning method using the combination: 3D clustering for the coarse localization phase and the compressive sensing-based fine localization. The method have three favorable features: (1) By using the previously estimated positions to define the sub-reference fingerprinting map (RFM) in the first coarse localization phase, the method can adopt the prior information that the users’ positions are continuous during walking to improve positioning accuracy. (2) The compressive sensing theory is adopted in the fine localization phase, where the positioning problem is transformed to a signal reconstruction problem. This again can improve the positioning accuracy. (3) The second coarse localization phase is added to avoid the global fingerprint matching in traditional 3D clustering-based methods when the stuck-in-small-area problem is encountered, thus, sufficiently lowered the maximum positioning latency. The proposed improved hierarchical WLAN positioning method is compared with two traditional methods during the experiments section. The resulting positioning accuracy and positioning latency have shown that the performance of the proposed method has better performance in both aspects.