A proactive approach for the retrieval of learning contexts without the use of sensor technologies

In this section, I address the research question – “Can a proactive approach for the retrieval of learning contexts without the use of sensor technologies be incorporated into a suggestion mechanism?”

I propose to use the learner’s learning schedule (i.e. electronic diary on a

mobile device) to retrieve their learning contexts. This is proactive and does not require the use of context-aware sensor technologies. The initial learning contexts intended to be retrieved from the learning schedule include thelocationandavailable

time(that a student has at a specific point in time). These two contexts are considered

because the study location of a student may affect their concentration and thus should be considered when selecting appropriate materials for students. Selecting an appropriate length of materials to learn/study according to students’ available time for study is important in the m-learning context. This is to ensure that learners have the opportunity to finish their learning task in the time available (Cui and Bull, 2005; Beckinget al., 2004; Martinet al., 2006c; Bouzeghoubet al., 2007). The retrieval of

these two learning contexts is important in an m-learning application in order to determine appropriate learning materials for students. Therefore, I wish to investigate a proactive approach without the use of context-aware technologies or requiring users to input these contexts ‘on the move’. Consequently, I developed the idea of using the learner’s learning schedule to find out their location and available time, at the time when they wish to carry out a learning/studying task.

I propose to add the following learning contexts to the framework – concentration level of the student and frequency of interruption (at a location). Cui

and Bull (2005) anticipated that these contexts can be successfully inferred from the location and collectively replace thelocation context. This is because the importance

location due to the possibilities of interruption. I wish to investigate whether the idea of using a learner’s learning schedule to retrieve their location and available time is realistically possible. Additionally, I examined whether the location context can indeed be replaced by the concentration level of the student and frequency of interruption (at the location). Results findings relating to this are detailed in 5.5.

The addition of two learning contexts from the user model is also proposed – LS and knowledge level. The reasons for the selection and addition of learning contexts are discussed in 4.3. The LS and knowledge level contexts cannot be automatically retrieved by means of technologies, context-aware or otherwise. These need to be input by the students.

The learning schedule approach relies on students capable of a) inputting all of their daily activities (including study-related and study-unrelated) into the learning schedule on a mobile device, b) keeping all of their scheduled activities up-to-date, and c) conforming to the activities as scheduled. Providing that these three requirements are met, the learning schedule is able to accurately retrieve the location and available time of a student (until their next scheduled appointment) at a particular point in time. I propose that the following information relating to a scheduled event should initially be recorded in the learning schedule –geographical location,type of

location (such as lecture theatre) (in order to ascertain the concentration level and

frequency of interruption contexts), start and finish time, type of event (such as

seminar) andnature of activity(study-related or study-unrelated).

I propose that the learning schedule approach would give further pedagogical benefits to students, in addition to the convenience of not requiring a) context-aware sensor technologies and b) input of parameters into the device ‘on the move’. These pedagogical benefits stemmed from the use of a diary for students as a time

management technique for their studies, especially for self-regulated students (Montalvo and Torres, 2004). In particular, it is argued that students are more likely to a) remember to attend their events and carry out their learning activities if the information regarding these is stored and could easily and regularly be referred to; b) be able to plan their study-related and study-unrelated events more effectively if information regarding their existing schedule could be viewed visually; and c) be able to self-motivate or self-regulate themselves through the act of planning their studies (Quenter et al., 2009). A self-regulated student can be characterized by their “active

participation in learning from the meta-cognitive, motivational, and behavioural point of view” (Montalvo and Torres, 2004). The characteristics of self-regulated students coincide with the attributes of higher-performance and higher-capacity students. More precisely, a self-regulated student would be able to perform the following (Winnieet

al., 2006; Chenet al., 2007; Hwanget al., 2006; Shihet al., 2007):

 Use cognitive strategies to organize, transform, elaborate and recover information.

 Direct their mental processes toward the achievement of personal goals through planning and control.

 Show positive emotions towards tasks and a high sense of academic self- efficacy, and have the ability to control these to adapt to the requirements of the task and of the specific learning situation.

 Plan and control the time and effort on tasks, and create and structure preferable learning environments such as identifying a suitable place for study and obtaining help from teachers and students when they experience difficulties.

 Use strategies to maintain their concentration, effort and motivation and avoid external and internal distractions whilst performing tasks.

Self-regulated students require both will and skill for the achievement and

attainment of their learning/studying processes (Ibid). My mCALS framework aims to support the skill part for students by determining which learning materials would be appropriate for them in the current situation. I believe that by considering these circumstances, the learning/studying processes of students can be improved.

In this section, I described the proactive approach of using a learner’s learning schedule to automatically identify their location and available time at the time when they wish to carry out a learning/studying task. This approach eliminates the need of context-aware sensor technologies and the requirement of students to input these context values at the time of usage i.e. ‘on the move’. Advantages of this approach were discussed. My mCALS framework is built upon the m-learning organizer works of Chanet al. (2004), Ryu and Parsons (2008) and Mirisaee and Zin’s (2009) – these

works are described in 2.7. However, their organizer does not use learning contexts to recommend learning materials to users based on different contexts, and this is the area I wish to focus on to make my research contributions.

4.3 Learning contexts which are significant in the recommendation of