The Usage of Mobile Learning: Comparative Studies among Technical and Vocational Education Students in Selected Universities

Che Ghani Che Kob,A Shah,Shangeetavaani Kannapiran

doi:10.3991/ijim.v14i05.13355

Abstract

This research is conducted mainly to examine the difference among Technical and Vocational Education (TVE) students at Universiti Pendidikan Sultan Idris (UPSI) and Universiti Tun Hussein Onn Malaysia (UTHM)on their computer proficiency, knowledge on mobile learning(ML), application preferred, and attitude towards ML. Quantitative research method via questionnaire distributed to 618 participants. T-test analysis was used to obtainthe differences among students in both the universities via Statistical Package for Social Sciences (SPSS) version 23.0 for Windows. The results showed there was no difference found among these students. Hereby, ML is vital for lifelong learning and the access to mobile devices are available for everyone these days. It has made ML to be a tool to learn regardless of time and place among the students today.

Highlights

This study here investigates the usage of mobile learning (ML) among Technical and Vocational Education (TVE) students in both the selected universities
Samples were from two different universities (UPSI and Universiti Tun Hussein Onn Malaysia (UTHM)) and certain duration given to answer the questionnaires
The spread of data deviated is similar upon the results shown since not much of difference noted

Summary

Introduction

This study here investigates the usage of ML among TVE students in both the selected universities. It clarifies the meaning of ML and enable students to explore their learning activities via usage of mobile devices. The main focus is on the usage of mobile devices among the students to accomplish their learning practices instead of just exploring on what and whether they are learning. The portability and affordability of mobile devices leverages the form learning in ML stated by Yamaguchi (2005). The advanced mobile technologies which are available like handheld devices, wireless technologies, high bandwidth infrastructure has started to extend e-learning towards ML mentioned by Sharples (2000)

Methods

Results

Conclusion