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This paper presents a novel 3-degrees-of-freedom (3-DOF) haptic master with 9 rubber bands for self-resetting. The mechanical design avoids coupling between three 10 directions mechanically by using three perpendicular axis intersecting at one point. Bevel 11 gear transmission is adopted to increase the compactness of the overall structure. 12 VR-based interactive system is designed and built by incorporating the proposed haptic 13 master. The proposed haptic device can generate force feedback along 3-degree-of-freedom 14 motion using motors and provide command signals to the avatar in the virtual 15environment. In order to analyze the performance of the developed device in terms of 16 haptic feedback operation, ergonomics assessments are designed and experimentally 17implemented. Preliminary studies on the influencing factor including the guidance force, 18 the reset force, the speed of the avatar and the arm the length have been conducted. The 19 results of this paper are of great significance for the design of the haptic master and 20 interactive system. 21Keywords: haptic master; force feedback; VR-based interaction; ergonomics assessments 22 23

An adaptive learning model for English vocabulary through a machine learning is proposed in this paper. The four main types of user information, including basic student information, quiz information, course video viewing information, and forum interaction information, are processed through feature engineering, and a better model on sparse data is proposed through comparison on different models, and the prediction accuracy of the model is improved through natural language processing techniques, to achieve feedback on user learning efficiency through user data and provide teachers and students with the corresponding teaching and learning suggestions for teachers and students. It is found that the quiz information has more influence than the course video viewing information, and the accuracy is improved by about 3% compared with TF-IDF after introducing word embedding. The use of mobile for English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus improving different aspects of learners' reading skills.