Applying Learning Object Libraries in K-12 Settings

Applying Learning Object Libraries in K-12 Settings

Sebastian Foti (University of North Florida, USA)
DOI: 10.4018/978-1-59904-861-1.ch035
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Abstract

The author describes the work of Dr. Mary Budd Rowe and the establishment of an early learning object databases. Extensive training with K-12 educators left two lingering issues about learning object library implementation: the question of granularity, and the perceptual chasm between developers of learning object libraries and the practitioners who will ultimately retrieve the objects. An examination of Dr. Rowe’s projects, including Science Helper K-8, Culture & Technology, and Enhanced Science Helper provides insight into possible barriers to success when teachers use learning object libraries as a tool for lesson planning. An intelligent lesson-planning tool that populates a student-centered learning environment is proposed as a possible solution to overcome such barriers.
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Introduction

Learning objects libraries have been around for well over 15 years and a great deal of effort has been put into their formalization. Indeed, it may be said that more attention is currently being directed towards the formation of such libraries than ever before. Planning groups, pilot programs, industrial libraries, and standards committees across the globe have been implemented and in some cases are running successfully. It is not altogether clear, however, if learning object libraries will ever be successful in mainstream K-20 educational settings. A “learning object is any entity, be it digital or nondigital that may be used for education and training” (IEEE, 2002, p. 6). A learning object library is a collection of such objects, along with facilities to retrieve them. Learning object libraries are the perfect computer application. They take the work of many and share it globally, make access to the materials simple and straightforward, catalog the materials by universally accepted standards, keyword, process themes, content themes, experience type, suitable learning style, and so on. It is a match made in Heaven: a world full of educational material, and a high speed, networked computer system. The merits of such a system are so obvious most advocates never even ask a very basic question. Unfortunately, we have learned that this question must first be asked: “If we build it, will they come?” Why wouldn’t they come? As it turns out, there may be many reasons.

Learning object libraries, like all database systems, have an inherent bias: they are categorical. They maintain information in very specific ways. And teachers do not necessarily think so categorically about their curricula. Even simple databases are rarely used professionally by teachers:

Database design can help users to think relationally, in a detailed fashion, and in an inductive (in aggregating data) and deductive (in disaggregating information) manner. Yet, the conceptual and technical difficulty of databases renders them invisible in terms of classroom use….Complex software such as spreadsheets, databases, simulation software, statistical programs, or “mind tools,” (so called because of their ability to promote higher order thinking) are most obvious in US classrooms by their absence. In tracking software use by 300 teachers with whom I worked over a four-year period, only about 12% reported spreadsheet use (mostly among math teachers and for purposes of creating graphs). When math teachers were removed from the equation, spreadsheet use fell to 2%. In eight years of classroom-based work with teachers, I have never witnessed database, GIS, simulation or statistical software use. (Burns, 2005, p. 3)

Apparently, problems with database use are not limited to teachers. In a study of collegiate business students researchers, Chen and Ray (2004) investigated the students’ ability to solve a realistic business problem using a database software application. Students made a variety of mistakes applying the database in their work. For example, “the majority of queries were unnecessary queries,” “6 of 11 individuals and 5 out of 9 teams performed no planning” (p.15) when using the database, and only one team and two individuals were able to make good conclusions” (p. 16). The researchers reported that “After exposure to numerous demonstrations and exercises involving database tasks such as creating queries, creating reports, and using online help facilities, students were not able to use these procedures to solve a business problem” (pp. 18-19). This suggests that understanding how to use database search facilities is not adequate preparation for solving problems that involve the use of the database.

Key Terms in this Chapter

Knowledge Base: A knowledge base is a database that is used to manage knowledge. The information in the knowledge base can be accessed using logical operators to determine appropriate retrieval items. Often a knowledge base uses an ontology or data model to define its classification scheme. As applied here, the knowledge base is a highly contextualized set of classifications and relationships related to a focused core of content knowledge.

Granularity: Granularity refers to the size of the components that make up a system. There is often discussion in learning object library circles about the ideal granularity of learning objects. The IEEE standards suggest several size ratings for objects from small media fragments to courses (or nested courses).

Metadata: Metadata is essentially data that describes data. In a learning object library, a database of metadata is used to “tag” each learning object for subsequent retrieval.

Inference Engine: An inference engine is a computer program that applies artificial intelligence to try to obtain answers or responses to queries from a knowledge base.

Science Helper Project: The Science Helper Project was a project funded by Carnegie Foundation of New York to archive curricular materials created in the post-Sputnik era in the United States. During this time, the U.S. National Science Foundation and other agencies funded several innovative science projects in the U.S. Ultimately, the project produced several products: (1) Science Helper K-8: a CD containing what would be called a Learning Object Library with a level-2 Aggregation Level (Lessons) by IEEE standards. The project contained 990 lessons. (2) Science Helper Video Series: A series of science videos focusing on elementary science methods for use by teachers, teacher’s aides, and interested parents. (3) Culture & Technology: A set of 3 CDs containing over 15,000 pages of searchable lesson material and 1550 lessons. Culture & Technology was the same type of learning object library as Science Helper K-8, but included social science curricula as well as science curricula. (4) Enhanced Science Helper: A revision of the original Science Helper CD with added learning objects including video clips. This library contained 1370 editable lessons and a more robust search engine.

Natural Language Processing: Natural language processing refers to the ability of computers to translate computer database information to natural sounding language or vice-versa (as is described in this discussion).

IEEE Standards: The Institute of Electrical and Electronics Engineers Standards Association is a developer of industry standards for a broad range of industries. The IEEE has created Learning Object Metadata standards which are referred to herein.

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