Page - 287 - in Intelligent Environments 2019 - Workshop Proceedings of the 15th International Conference on Intelligent Environments

each network is different in nature and design. They can be divided into four main groups: bandwidth, availability, delay, loss and error. [10] From the Chart, it can be seen that the majority of students, 81.3 %, agree that the learning content will be adapted according to the Network performance, because it helps them to learn even if the network was slow and it provides them an easy access to the learning content, 18.8 % of them said no, because they think that it will not make too much difference, and information is already easily accessible. Others think that a slight delay isn't much of an issue to warrant learning to be adapted to slow internet. Question 9: I agree that applying location-based techniques can assist my learning. Figure 9. Students’ opinion about the use of the location-based techniques to personalise learning content Since learning becomes mobile, location becomes an important context, both in terms of the physical whereabouts of the learner and also the opportunities for learning to become location-sensitive. The properties and affordances of one’s location vary enormously and hence other contexts become even more important, such as the task or goal or the user; the ubiquity of network access (GPS, wifi etc); the time of the year or day or even the weather. [11] The results reveal that 15.6 % of students are strongly agree, and 37.5 % of them agree that applying location-based techniques can assist their learning, because it provides easier workflow efficiency, also it allows more things to be tailored towards them. The results also show that 39.1 % of students are neutrals and 7.8 % of them are disagrees, because they see that their location doesn't really matter when it comes to learning, also location can be spoofed, causing fraud. 5. Discussion To understand the most important learning context from the learners’ perspective in the learning process we have to use the 5 point Likert Scale, with numerical values ranging from, to give the weight to the responses. For example, for the question about personal preferences the total responses is 64 and the scale range is 1= strongly disagree, 2= disagree, 3= neutral, 4= agree and 5= strongly agree and from our data 0 students are strongly disagree, we will then have 0x1=0, 1 student disagree (1x2=2), 19 students neutral (19x3=57), 33 students agree (33x4=132) and 11 students strongly agree K.Chouchaneetal. /Context inMobileLearning: ThePointofViewof theLearners 287