A Knowledge-based Approach to Retrieving Teaching Materials for Context-aware Learning

Introduction With the rapid development of wireless communication and sensor technologies, ubiquitous learning (u-learning) has become a promising solution to educational problems which can sense the situation of learners and provide adaptive supports to students (Chen et al., 2007; Hwang et al., 2008; Kuo et al., 2007; Si et al., 2006; Yang, 2006). Context-awareness is the major characteristic of u-learning, and the situation or environment of a learner situated can be sensed. There are twofold advantages of context-aware learning. One can alleviate environmental limitations, and the other can utilize available resources to facilitate learning. There are several types of applications for context-aware u-learning. A typical scenario is "learning with on-line guidance" as presented in Hwang's research (2006), which focused on the "identification of plants" unit of the Nature Science course in an elementary school. The context is in campus, and the human-system interaction is described as follows: * System: Can you identify the plant in front of you? * Student: Yes. * System: What is the name of this plant? * Student: Ring-cupped oak. * System: Do you see any insect on it? * Student: Yes. * System: Can you identify this insect? * Student: No. * ... The assumption is that the system is aware of the location of the student and her/his nearby plants by sensor technologies and built-in campus maps. Learning activities in ubiquitous environments are directed by instructional strategies which are general approaches instead of specific methods. As shown in Figure 1, instructional activities are generated according to instructional strategies originated from pedagogic theories. Designers of learning activities should utilize the advantages of u-learning environments to realize pedagogic goals. [FIGURE 1 OMITTED] The context-aware learning content retrieval problem is motivated by the following assumptions: * Students' learning performance can be improved by providing right content at right time and right place. * During ubiquitous learning, students' queries are usually related to knowledge of their nearby objects. Retrieval of learning content, hereafter named Content Retrieval (CR), is an important activity in u-learning, especially for on-line data searching and cooperative problem solving. Furthermore, both teachers and students need to retrieve learning content for teaching and learning respectively. However, conventional keyword-based content retrieval schemes do not take context information into consideration, and therefore they cannot fulfill the basic requirements of u-learning to provide users with adaptive results. To support context-aware learning, learning contents need to be provided according to learners' contexts. For example, when a student can not identify an insect in the u-learning course, s/he can access a learning object repository for more information by submitting a query. Thus we can imagine that queries are most likely ambiguous and need refinement. If context information can be applied to refine the original query, it will be easier for learners to retrieve relevant contents. As shown in Figure 2, we classify the schemes of content retrieval into static and dynamic types according to the adaptability of the retrieved results. For static CR, the retrieved result only depends on the query regardless of users and contexts. Nevertheless, dynamic CR can be further divided into personalized, context-aware, and other schemes according to the factors considered by the adaptive mechanisms of CR. The static CR is adapted to subjective factors of learners, such as user profile, preference, etc. In other words, the same query submitted by different persons could result in different results retrieved. On the other hand, context-aware CR is adapted to objective factors of learners, like time, place, device, activity, and peers etc. …