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Student mobility program have risen to the top of local universities' worldwide priorities. In addition to giving university students the chance to learn about different cultures, this programme helps expose them to various study strategies. Mobility programmes should adopt a new strategy in the age of Revolution Industry 4.0 and use technology as the vehicle for implementation. This study aims to determine whether virtual mobility programmes are appropriate for implementation, the level of students' knowledge regarding virtual mobility implementation, the reasons why students joined virtual mobility programmes, and the kinds of media that students anticipated using when participating in virtual mobility. Quantitative method was used with survey questionnaire to collect the data. A total of 304 students of Malaysian Technical Higher Institution were involved as a sample in this study also descriptive and inferential analysis were employed to this study. The findings showed that virtual mobility can be seen as an alternative for conventional mobility and it still has an impact on students’ soft skills. Besides, the data showed that the level of students’ knowledge towards the implementation of virtual mobility is moderate and majority of the respondents decided to join the virtual mobility program because they could develop their information technology skills. Meanwhile, most of the respondents expected to use internet as a main medium to join the virtual mobility program.

Student mobility program have risen to the top of local universities' worldwide priorities. In addition to giving university students the chance to learn about different cultures,n this programme helps expose them to various study strategies. Mobility programmes should adopt a new strategy in the age of Revolution Industry 4.0 and use technology as the vehicle for implementation. This study aims to determine whether virtual mobility programmes are appropriate for implementation, the level of students' knowledge regarding virtual mobility implementation, the reasons why students joined virtual mobility programmes, and the kinds of media that students anticipated using when participating in virtual mobility. Quantitative method was used with survey questionnaire to collect the data. A total of 304 students of Malaysian Technical Higher Institution were involved as a sample in this study also descriptive and inferential analysis were employed to this study. The findings showed that virtual mobility can be seen as an alternative for conventional mobility and it still has an impact on students’ soft skills. Besides, the data showed that the level of students’ knowledge towards the implementation of virtual mobility is moderate and majority of the respondents decided to join the virtual mobility program because they could develop their information technology skills. Meanwhile, most of the respondents expected to use internet as a main medium to join the virtual mobility program.

Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods. The DSL Access Network is emulated in the laboratory to accommodate VDSL2 Technology with various types of cable fault along the cable distance between 100 m to 1200 m. Firstly, the line operation parameters and loop line testing parameters are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to pre-process the fault data. Then, the random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault data. Finally, the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network System. The results show that the cable fault detection has an accuracy of more than 97%, with less minimum absolute error in cable fault localization of less than 11%. The proposed algorithm may assist the telecommunication service provider to initiate automated cable faults identification and troubleshooting in the DSL Access Network System.