Students’ Use of Knowledge Resources in Environmental Interaction on an Outdoor Learning Trail

This study examined how students leveraged different types of knowledge resources on an outdoor learning trail. We positioned the learning trail as an integral part of the curriculum with a pre- and post-trail phase to scaffold and to support students’ meaning-making process. The study was conducted with two classes of secondary two students. We coded two groups’ discourse to examine the use of knowledge resource types in the meaning-making process in an outdoor learning setting: contextual resource, new conceptual resource, prior knowledge resource, as well as the relationship among these knowledge resource types. Next, we also examined environmental interaction and integration in the students’ use of these knowledge resource types. Analysis showed that contextual resources are chiefly instrumental in fostering students’ capacity to harness new conceptual resource and to activate prior knowledge resource in interacting with and integrating the outdoor learning environment in the meaning-making process.

Comparing architectural styles for distributed expert knowledge modules in intelligent tutoring systems

Intelligent Tutoring Systems (ITSs) are computerized systems for learning-by-doing. These systems provide students with immediate and customized feedback on learning tasks. An ITS typically consists of several modules that are connected to each other. This research focuses on the distribution of the ITS module that provides expert knowledge services. For the distribution of such an expert knowledge module we need to use an architectural style because this gives a standard interface, which increases the reusability and operability of the expert knowledge module. To provide expert knowledge modules in a distributed way we need to answer the research question: ‘How can we compare and evaluate REST, Web services and Plug-in architectural styles for the distribution of the expert knowledge module in an intelligent tutoring system?’. We present an assessment method for selecting an architectural style. Using the assessment method on three architectural styles, we selected the REST architectural style as the style that best supports the distribution of expert knowledge modules. With this assessment method we also analyzed the trade-offs that come with selecting REST. We present a prototype and architectural views based on REST to demonstrate that the assessment method correctly scores REST as an appropriate architectural style for the distribution of expert knowledge modules.