Evaluation of a mobile learning organiser for university students

2005 ◽  
Vol 21 (3) ◽  
pp. 162-170 ◽  
Author(s):  
Dan Corlett ◽  
Mike Sharples ◽  
Susan Bull ◽  
Tony Chan
2014 ◽  
Vol 519-520 ◽  
pp. 1667-1670
Author(s):  
Huan Cao ◽  
Zhao Hui Guo

Mobile learning based on smart phones has become a hot topic in research.In this paper, questionnaires and interviews were applied to survey the state of Chinese university students' mobile learning by using smart phones, and 360 students from 5 universities in Wuhan were chose as samples.The research discovers that the students' level of understanding about mobile learning is still low.Even though most of them possess smart phones with Android system, they seldom use smart phones to learn their major in after-class time.To improve the performance of mobile learning by using smart phones,the R&D of smart phones should be strengthened, more abundant learning APP should be developed and the coverage of wifi should be widened.


2020 ◽  
Vol 12 (20) ◽  
pp. 8618
Author(s):  
Quadri Noorulhasan Naveed ◽  
Mohammad Mahtab Alam ◽  
Nasser Tairan

Advanced mobile devices and global internet services have enhanced the usage of smartphones in the education sector and their potential for fulfilling teaching and learning objectives. The current study is an attempt to assess the factors affecting mobile learning acceptance by Saudi university students. A theoretical model of mobile learning acceptance was developed based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) model. Theoretically, five independent constructs were identified as most contributory towards the use of mobile learning and tested empirically. Data were collected through an online survey and analyzed using SmartPLS. The results of the study indicate that four constructs were significantly associated with mobile learning acceptance: perceived usefulness (β = 0.085, t = 2.201, and p = 0.028), perceived ease of use (β = 0.031, t = 1.688, and p = 0.013), attitude (β = 0.100, t = 3.771, and p = 0.037), and facilitating conditions (β = 0.765, t = 4.319, and p = 0.001). On the other hand, social influence was insignificant (β = −0.061, t = 0.136, and p = 0.256) for mobile learning acceptance. The contribution of social influence towards the use of mobile learning was negative and insignificant; hence, it was neglected. Thus, finally, four constructs (perceived usefulness, perceived ease of use, attitude, and facilitating conditions) were considered as important determinants of mobile learning acceptance by university students.


2018 ◽  
Vol 11 (2) ◽  
pp. 178-191 ◽  
Author(s):  
Charles Buabeng-Andoh

Purpose The purpose of this paper is to explore the ability of the integration of technology acceptance model (TAM) and theory of reasoned action (TRA) to predict and explain university students’ intention to use m-learning in schools. Design/methodology/approach In total, 487 students participated in this study. A seven-likert scale survey questionnaire which comprised of 23 items was completed by the students. Structural equation modeling was used as the statistical technique to analyze the data. Findings The study found that the resulting model was fairly able to predict and explain behavioral intention (BI) among students in Ghana. In addition, this study found that attitudes toward use and subjective norm significantly influenced students’ BI to use mobile learning. The model explained 23.0 percent of the variance in BI, 33.8 percent in perceived usefulness and 47.6 percent in attitudes toward use. Of all the three endogenous variables, attitude had the greatest effect on BI. Originality/value Although, the above-mentioned models have been adopted in many studies, few or none have combined TRA and TAM as a research framework to predict and explain students’ intention to use m-learning since m-learning is fairly new in educational environments. Therefore, a model that combines all constructs from TRA and TAM was proposed in this study to explore university students’ intention to use m-learning in schools.


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