Experiential Teaching is more Conducive to Student Learning than Traditional Teaching

Some drawbacks of traditional teacher-centred teaching have gradually become apparent and have led to a growing interest in experiential teaching. Students and teachers are prominent participants in the teaching process, and their behavioural performance largely determines the learning outcomes. Likewise, a harmonious teacher-student relationship is essential to students’ learning experience. In this paper, we have analysed the impact of experiential teaching on three aspects: student engagement, teacher-teaching innovation and teacher-student collaboration, to show that experiential has clear advantages for student learning. However, some argue that the design and practice of experiential teaching are challenging for teachers and that the student-centred approach is not conducive to managing classroom order. This, therefore, suggests new elements for teacher education and schoolteacher training to avoid the gap between instructional design and practice. And further research is needed by educational researchers on how to manage classroom order in experiential teaching and to make students learn effectively

The Text Analysis of National Education Policy 2020

This paper first briefly introduces the National Education Policy 2020 (NEP) document formulated by the Indian government and the five education theories (traditional academic theory, learner-centred theory, human capital theory, social efficiency theory and social reconstruction theory) in the field of education. Through relevant literature review, five educational theories or ideologies are proven that they cannot be fully applied in pedagogy. As such, this paper proposes a refutation perspective for each educational theory, showing my adequate understanding and critical awareness of education research. After that, these five significant educational theories are used to analyse the NEP contents from pages 1 to 32. Combining my direct observation and thematic coding of NEP text with the focus ideas of five education theories, the final result of this paper illustrates that social efficiency theory is most dominant in NEP; thereby, the Indian government needs to consider how to balance their education ideology. At the end of this work, I show personal consideration regarding the NEP measures that target India’s current focus social issues: Indian educators still must test and justify the rationality and effectiveness of these education policies formulated in NEP by future practices

Higher Education in China should Increase the Proportion of Practical Teaching

With the evolution of society, the demands on human resources have also changed significantly. In the face of such changes, the field of education also needs to adapt. Current higher education in China, influenced by Confucianism, places more emphasis on theory than practice, leading to a lack of applied, innovative, and complex talents in the market, so the proportion of practical teaching should be increased. By discussing the benefits of practice for knowledge, talent development, and employment, this paper illustrates the important role of increasing the proportion of practical teaching in Chinese higher education today, guiding students to become applied, innovative and complex talents with solid theory and good quality and suggesting improvements in response to the problems currently faced by practical teaching

Advancing Android Activity Recognition Service with Markov Smoother: Practical Solutions

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications

Sensor-based activity recognition with dynamically added context

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods

A Multi-objective Particle Swarm Optimization Algorithm Based on Reverse Learning

In order to solve the contradiction between population diversity and convergence in particle swarm optimization algorithm, in this paper, a particle swarm optimization algorithm with reverse learning is proposed. On this basis, the values of learning factor and constraint factor parameters are modified, and the linear decreasing weight strategy was adopted. By modifying the learning factor and the constraint factor, the algorithm improves the particle optimization ability. It balances the global search and local search of the particle, and the convergence speed is improved by using the inertia weight. When it is detected that the algorithm falls into the local optimal region, the position information of these poor particles is used to guide some particles to reverse learning at a faster flight speed, and the particles are quickly pulled out of the local optimal region. The reverse learning process can not only improve the diversity of particle population, but also ensure the global detection ability of the algorithm. Experimental results show that, compared with the basic MOPSO algorithm, this algorithm has fast convergence speed and high solution accuracy in function optimization

Activity recognition with weighted frequent patterns mining in smart environments

In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities