Learning contexts which are significant in the recommendation of appropriate learning materials

In this section, I address the research question – “Which learning contexts are significant in the recommendation of appropriate learning materials?”

To decide upon which learning contexts are most significant in the recommendation of appropriate learning materials, I examined the works of Cui and

Bull (2005) and Martin et al. (2006b), which are most related to my framework –

these works were described in 2.5. I selected five learning contexts to be incorporated into the framework - LS, knowledge level, concentration level, frequency of

interruption and available time. The latter four contexts were utilized in the work of

Cui and Bull (2005), and the LS, knowledge level and available time contexts were utilized in the work of Martin et al. (2006b). The four scenarios in 4.1 illustrate the

types of materials that may be appropriate for students with different levels of Java proficiency and available time, at the time of learning/studying. The reasons for the proposal of the incorporation of these five learning contexts are presented below.

LS – The importance of incorporating cognitive learning contexts into the design and development of context-aware m-learning applications has been emphasized by many authors (Prekop and Burnett, 2003; Beale and Lonsdale, 2004). This dimension of context has often been neglected in the design and development of learning applications. The dimension includes LS/preferences/strategies, knowledge level, user’s goals, personality and characteristics etc. Learners may have different preferred styles of learning and psychological attributes, which were shaped by their learning experiences. These should be taken into consideration, especially during m- learning (Parsons et al., 2006). A more enjoyable and effective learning experience

for learners can be created by matching the correct level of information according to the learner’s most preferred learning style (Beale and Lonsdale, 2004). In contrast to this view, critics maintained that no difference was made in the level of the students’ abilities to learn/study, whether they used materials that suited their LS or not (Coffieldet al., 2004). However, we propose that many students can benefit from the

selection of learning materials based on their LS; hence this learning context should be incorporated. Extensive research results have been obtained by Graf (2007), which

established, via two evaluative studies that a relationship did exist between a learner’s LS (as defined by the dimensions of the Felder and Silverman LS model, 1988) and their working memory capacity. It was found that learners with a balanced learning style for the active/reflective and the sensing/intuitive dimension, and those with a verbal learning style, tend to have a higher memory capacity. Learners with high working memory capacity may be those with a verbal or visual learning style. I propose to use the Felder and Silverman learning style model (1988) because this has been frequently used in e- and m-learning systems. As discussed earlier, different learners have different goals and LS, and it is important that these are taken into consideration in an m-learning application.

Knowledge level – The selection of materials appropriate to a student’s level

of knowledge can enhance their effectiveness of learning/studying the materials (Cui and Bull, 2005; Martinet al., 2006c; Becking et al., 2004; Bouzeghoub et al., 2007)

because students a) may become bored and unmotivated if materials are too uncomplicated and repetitive of concepts that they already know and/or understand, and b) may not be able to progress if materials are too advanced for them; this is ineffective and could cause additional stress to students. I propose that many students can benefit from the selection of learning materials based on their knowledge level – so that they do not have to re-learn materials that they already know or have to tackle problems that are too advanced for them.

Concentration level – Selecting learning materials based on the student’s

concentration level is important (Cui and Bull, 2005; Becking et al., 2004). The

organizer’s system requests the user to input their perceived level of concentration (as high, medium or low) at the beginning of a learning session. Together with the other contexts deployed in their system, this context determines the materials selected for a

student for that particular session. A student’s level of concentration could be lower, more unstable and prone to interruptions during m-learning. This is due to a potential 1) higher level of noise, and 2) busier environment with more possible distractions such as people coming and leaving. I propose that students working in mobile environments can benefit from having materials recommended to them based on the level of their concentration.

Frequency of interruption – Similar to the concentration level of a student,

the frequency of interruption can be higher and more unpredictable during m-learning. For example, the frequency of interruption in a café is likely to be higher than that in a library (Cui and Bull, 2005; Beckinget al., 2004; Martinet al., 2006c). The frequency

of interruption in a location may affect a student’s concentration level, and hence I propose that students working in mobile environments can benefit from having materials recommended to them based on the frequency of interruption at that location. Cui and Bull’s (2005) system also incorporated this context and requests students to input their perceived frequency of interruption at that location, at the beginning of the learning session.

Available time – I propose that a student’s available time should be used as

one of the bases for recommendation of appropriate learning materials to them during m-learning. This is so that an adequate amount and/or size of learning materials can be appropriately recommended to them (Cui and Bull, 2005; Becking et al., 2004;

Martinet al., 2006c).

The LS and knowledge level contexts have been deployed frequently in adaptive e- and m-learning applications (Grigoriadou et al., 2006). Therefore, these

are significant internal (to the user) learning contexts that should be considered (see 2.4.2). The replacement of these contexts by other similar ones such as learning

strategies or another learning style model would not invalidate the framework. Similarly, I consider the concentration level and frequency of interruption contexts in the framework because these appear to be important factors that should be taken into account in m-learning. Similar contexts can be used to replace these such as frequency of distractions, or perceived level of distractions. The available time context should be considered in the suggestion of m-learning materials to students.

At this point in the thesis, I have not considered certain other contexts to be important or relevant to the framework. These are, for example, users’ current activities, mobile devices being used, noise and temperature of the environment and so on. A user’s current activity is not considered relevant to the framework because it is assumed that they are interested in undertaking a learning task when they are using the system. Therefore, their activity is to undertake a learning task. Different types of mobile devices are not considered important to the framework because it is the pedagogical aspects of contexts I wish to focus on, rather than the technological aspects of different mobile devices.

Noise and temperature may potentially affect a student’s concentration level; however I do not include these because these are already covered by the concentration level context, which is to be used in my framework. I am currently not aware of learning contexts such as noise and temperature, which are used in context-aware m- learning suggestion mechanism frameworks/applications. However, these have been used for mobile non-learning applications such as in Rarau et al. (2005) and Costaet

al. (2006). An initial design of our framework used a microphone to detect the noise

attribute (Yau and Joy, 2007), however, after deliberation, the noise context was removed because the data analysis from my interview study showed that different students were equally potentially distracted or not distracted from the same level of

noise. I decided that this noise context should be replaced by the concentration level context.

4.4 The types of learning materials which are appropriate for