A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study

A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study

Jane Yin-Kim Yau (University of Warwick, UK) and Mike Joy (University of Warwick, UK)
Copyright: © 2009 |Pages: 27
DOI: 10.4018/jmbl.2009090803
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Mobile learning applications can be categorized into four generations: non-adaptive, learning-preferences based adaptive, learning-contexts-based adaptive and learning-contexts-aware adaptive. The research on our learning schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using them as the basis for selecting appropriate learning materials. We have adopted a pedagogical approach for evaluating this framework, an exploratory interview study with potential users consisting of 37 university students. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Our data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario and a proposed taxonomy for describing mobile learner preferences.
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The importance of the deployment of learning contexts into mobile learning (m-learning) applications, and making these applications aware of the context (or circumstances) it is being used in, is currently a very significant part of their development (Sharples, 2006). Since portable mobile devices can be used for learning anytime anywhere, learners have the flexibility to choose practically any location that suits them. For a full-time university student living on campus, this choice may not seem as crucial as for a part-time student who has family and work commitments and commutes onto campus every day, for example. The latter student typically has many more time limitations than the former, and because of this constraint, it is much more important and necessary for the latter student to be able to utilize whatever available time they have and to be able to learn and study at any location. For example, it might be necessary for them to make use of the time when they are commuting each day on public transport. Becking et al. (2004) also noted similar difficulties that distance learning students face because of this time constraint.

Given the possible different circumstances surrounding the learner at the point of learning and studying (such as the location, length of time available, their concentration at that point in time, or the frequency of interruption at the location), there may be pedagogical benefits to the learner if their m-learning application were to be aware of these circumstances and be able to suggest appropriate materials for the learner’s context. Our research is motivated by the fact that students may want to carry out their learning tasks and activities at every given opportunity with sufficient time available. Naturally, this may not always hold true; however, as argued by Kukulska-Hulme and Traxler (2005) “Learning outside a classroom or in various locations requires nothing more than the motivation to do so wherever the opportunity arises.”

In this article, we describe the design of a theoretical framework to support those students who wish to carry out their learning at different locations with variable amounts of time available to them. Our goal is to recommend (the most) appropriate activities to them, given the particular circumstances, in an attempt to maximize their learning productivity. In achieving this goal, the following three research questions will need to be resolved. The motivation for resolving these is also described.

  • 1.

    Can a proactive method of retrieving learning contexts, without the use of context-aware sensor technologies, be successfully established? Our theory investigates the possibility of substituting context-aware sensor technologies with a simple, yet efficient, technique: the learner’s learning schedule. This relies on the self-discipline of students to tell their mobile device their probable learning schedule ahead of time. The device will then suggest appropriate study measures to the user at each particular point in time.

The possible methods used for retrieval of learning contexts have been investigated in many related m-learning studies and are divided into direct and implicit retrieval methods. Direct retrieval from the user (see Cui & Bull, 2005) requires time and effort and may interfere with what the user is doing. Alternatively, retrieval may be done implicitly by using sensors to detect the features of different learning contexts (see Schmidt, 2002).

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