Instructional Use of Learning Objects

This chapter describes and discusses the application of collaborative filtering techniques to the design of metadata structures for learning objects, and its implications for instruction. This approach enables context-sensitive discovery and recommendation of learning objects. The discussion is based upon research in developing and evaluating a collaborative filtering system, which enables users to share ratings, opinions, and recommendations about resources on the Web. An additional benefit of this approach is that it also allows a user to locate other users who share similar interests for further communication and collaboration. Introduction Much recent research has focused on building Internet-based digital libraries, containing vast reserves of information resources. Within educational applications, a primary goal of these libraries is to provide users (including teachers and students) a way to search for and display digital learning resources, frequently called learning objects. As part of these efforts, researchers are developing cataloging and tagging systems. Much like labels on a can, these tags provide descriptive summaries intended to convey the semantics of the object. Together, these tags (or data elements) usually comprise what is called a metadata structure (LTSC, 2000). Metadata structures are searchable and thus provide means for discovering learning objects of interest, even when these are nontextual. For example, an IEEE standards committee, called the Learning Technologies Standards Committee (LTSC), has developed a draft standard for “Learning Objects Metadata.” For the purpose of this task, the committee defined a learning object as “any entity, digital or non-digital, which can be used, re-used or referenced during technology-supported learning” (LTSC, 2000). The LTSC standard is designed to provide a means of enhancing the discovery of learning objects. The LTSC learning object model currently defines over 50 data elements within its hierarchical metadata structure. Example data elements include title, language, rights management, and description (LTSC, 2000). Because of their status as official data descriptors of learning objects, we call these authoritative data elements (Recker & Wiley, 2000). The LTSC standards are clearly focused on addressing knowledge management issues of learning object repositories. The standards are particularly focused on solving the technical aspects of object description, and cataloging within a networked environment. They are not, however, focused on capturing aspects surrounding the initial context of instructional use of objects. They do not support encoding a description of learning activities and context surrounding a learning object. The standards also do not provide explicit support for the re-use of learning objects within specific instructional contexts. In this paper, we propose an alternate view of creating and sustaining a metadata structure for distributed digital learning objects. In particular, this paper describes and discusses the application of collaborative filtering techniques within a metadata structure for describing and cataloging learning resources. As we will describe, the approach supports metadata structures that incorporate what we call non-authoritative data elements. This form of metadata attempts to capture the context of use and surrounding activities of the learning object. The data elements can also describe the community of users from which the learning object is derived. Moreover, any user (and not just the authorized cataloger) can contribute a metadata record. As a result, a particular learning resource may have multiple non-authoritative metadata records, in addition to its authoritative record. As we will explain, such an approach supports discovery and automatic filtering, and recommendation of relevant learning objects in a way that is sensitive to the needs of particular communities of users interested in teaching and learning. An additional benefit of this approach is that it allows a user to locate other users (students or instructors) who share similar interests for further communication and collaboration. In the next section of this paper, we describe collaborative filtering and its implementation within a system called Altered Vista. We then describe an implemented example and present results of pilot user studies. We conclude with a discussion of planned system extensions, applicability to a framework for designing learning object metadata structures, and a discussion of implications for teaching and learning. 244 5.1: Recker, Walker, & Wiley