Multi-ethnic computational thinking and cultural respect in unmanned aerial vehicle-assisted culturally responsive teaching.

Introduction: As education systems worldwide begin to accept and implement computational thinking, the educators of both elementary and higher education are considering the cultivation of students' computational thinking abilities. It is hoped that students effectively analyze and deconstruct all kinds of complex issues with computational thinking, and seek computer-executable ways to solve real-world problems. Through the integration of program education, students can learn and develop the abilities to practically apply their theoretical learning in information technology education. With the promotion of the concept of multicultural education, an increasing number of educational arenas are gradually introducing the concept of multicultural education to inculcate in students respect for different ethnic cultures via multicultural integration. Methods: In this study, unmanned aerial vehicle (UAV) technology was used to introduce culturally responsive teaching. The objective was to build a UAV-assisted culturally responsive teaching environment for multi-ethnic students that is based on their different thinking mechanisms formed by their respective cultures and living environments. Multi-ethnic students can attempt to solve problems employing computational thinking that is implemented when programing to control UAV. With the influence of culturally responsive teaching, the UAV-assisted learning strategies helped students and teachers of multi-ethnic groups understand different cultures and learn through mutual aid and cooperation. Results and Discussion: This study discussed the computational thinking abilities via different dimensions: logical thinking, programming ability, and cultural respect. The results showed that the introduction of UAV-assisted culturally responsive teaching method benefits not only indigenous students. For Han Chinese students as well, owing to the influence of cultural understanding, their overall learning effectiveness and cultural respect will be strengthened. Thus, this method improves the learning effectiveness in programming of multi-ethnic students, as well as of students with weaker prior programming ability. The method can also enhance the cognition and comprehension of different cultures in multicultural education.

Integrating Multiculturalism Into Artificial Intelligence-Assisted Programming Lessons: Examining Inter-Ethnicity Differences in Learning Expectancy, Motivation, and Effectiveness.

Given the current popularization of computer programming and the trends of informatization and digitization, colleges have actively responded by making programming lessons compulsory for students of all disciplines. However, students from different ethnic groups often have different learning responses to such lessons due to their respective cultural backgrounds, the environment in which they grew up, and their consideration for future employment. In this study, an AI-assisted programming module was developed and used to compare the differences between multi-ethnic college students in terms of their theoretical and actual learning expectancy, motivation, and effectiveness. The module conducted analysis through the deep learning network and examined the relevant processes that the students underwent during programming lessons, as well as the types of errors they had committed. Their learning motivation for and actual learning performance in programming were then examined based on the cognitive learning theory. The results of the experiment, which involved 96 multi-ethnic college students, indicated that the two groups had dissimilar theoretical performance in terms of their expectancy and motivation for learning programming. The indigenous students' main concern was whether programming would affect their families or tribes, and this concern affected and was reflected in their learning outcomes. In contrast, the learning motivation and goals of Han Chinese students were driven by the cognition of the value of programming to themselves. The research findings can contribute toward the cognition and understanding of multi-ethnic students when learning computer programming and development of the appropriate teaching methods, and serve as a reference for subsequent research on integrating multiculturalism into computer programming lessons.

Design and Analysis of a Pitch Fatigue Detection System for Adaptive Baseball Learning.

Owing to the rapid development of information and communication technologies, such as the Internet of Things, artificial intelligence, and computer vision, in recent years, the concept of smart sports has been proposed. A pitch fatigue detection method that includes acquisition, analysis, quantification, aggregation, learning, and public layers for adaptive baseball learning is proposed herein. The learning determines the fatigue index of the pitcher based on the angle of the pitcher's elbow and back as the number of pitches increases. The coach uses this auxiliary information to avoid baseball injuries during baseball learning. Results show a test accuracy rate of 89.1%, indicating that the proposed method effectively provides reference information for adaptive baseball learning.

Analysis of AI Precision Education Strategy for Small Private Online Courses.

In recent years, the learning efficacy of online to offline (O2O) teaching methods seems to outperform traditional teaching methods in the field of education. Students can use a small private online course (SPOC) teaching platform to preview class-related materials, learn basic knowledge, and enhance the practical experience of system development in offline courses. The research team applied an artificial intelligence (AI) precision education strategy to design a teaching experiment that evaluated whether this approach may lead to better learning outcomes. In addition to questionnaire surveys to ascertain students' attitudes toward and their satisfaction with learning, this study employed in-depth interviews to understand a potential influence on changes in teachers' curriculum design and teaching approaches when SPOCs was integrated into the traditional university classroom, as well as the impact of the AI precision education model. The results showed that the AI precision education model may facilitate students' learning experience and enhance student achievement.

Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning.

Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.

Research on Head-Mounted Virtual Reality and Computational Thinking Experiments to Improve the Learning Effect of AIoT Maker Course: Case of Earthquake Relief Scenes.

In this study, the head-mounted virtual reality (VR) technology is adpoted for computational thinking teaching in the AIoT Maker course teaching. The earthquake relief situation is designed in the VR in the course scenario, because in the context of situational thinking, pre-emptive training in the face of emergency disasters has been conducted through observation meetings or training courses. Through listening to lecturers or experienced personnel to share experiences, students often have a harder time thinking about real scenes and it is harder to think creatively how to design with the emergency disaster response. In view of this, this research will combine the development and evaluation of earthquake relief training courses for head-mounted VR and computational thinking experiments to explore the use of VR and computational thinking experiments to drive students to create ideas for real disaster relief scenarios. Through computational thinking, students think about different script situations and discuss in each scene to find a suitable maker design of the AIoT project. Finally, this study combined with its modular space program training to develop students' programming skills. According to the experiment, this study is able to strength students' practical learning motivation, and follow-up employ ability training for course learning.

An Intelligent Body Posture Analysis Model Using Multi-Sensors for Long-Term Physical Rehabilitation.

Sensors can be installed on various body parts to provide information for computer diagnosis to identify the current body state. However, as human posture is subject to gravity, the direction of the force on each limb differs. For example, the directions of gravitational force on legs and trunk differ. In addition, each person's height and structure of limbs differs, hence, the acceleration and rotation resulted from such differences on force and length of the limbs of a person in motion would be different, and be presented by cases of different postures. Thus, how to present body postures through skeleton system equations, and achieve an long-term physical rehabilitation, according to the different limb characteristics of each person, is a challenging research issue. This paper proposes a novel scheme named as "Intelligent Body Posture Analysis Model", which uses multiple acceleration sensors and gyroscopes to detect body motion patterns. The effectiveness of the proposed scheme is proved by conducting a large number of practical experiments and tests.

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.