Building knowledge structures for online instructional/learning systems via knowledge elements interrelations
Introduction
The OnLine Instructional/Learning Systems (OLI/LS) have been successfully used to provide the learners with an environment of one-to-many instruction, learning without limited time and place, different kinds of knowledge sources, open learning systems, information delivery with multimedia format, cooperative learning, and storing and quickly accessing huge amount of learning materials (Burbules and Callister, 1996, Penfield and Larson, 1996, Sun and Chou, 1996, Paolucci, 1999, Grabe and Sigler, 2002). In order to have the online materials to be reusable, accessible, durable, interoperable, adaptable, and affordable, the organization of Advanced Distributed Learning Initiative (ADL) has designed and developed a model (Sharable Content Object Reference Model, SCORM) to help produce OLI/LS materials (http://www.adlnet.org). Basically, the SCORM contains two major parts. One is content aggregation model (CAM) and the other is run-time environment (RTE). In CAM, particularly, the strategy that SCORM takes on for managing learning materials is to define, design, and develop standardized learning objects as knowledge elements of learning subjects. Thereafter, when producing an OLI/LS course, an instructor can pick up some (or all) of these learning objects and then organize them into a course package. Importantly, it has been seen that the SCORM do provide the OLI/LS with a meaningful methodology in modeling learning materials (Huang et al., 2002).
However, it is generally believed that to arrange knowledge elements with respect to the sequences for an OLI/LS is a complex, but important task to learners, in particular to a beginner. When using an OLI/LS, a beginner may not have the basic knowledge about what is the next step he/she should go, and thereafter may travel several miles in width, but 1 in. in depth. Consequently, major issues that include low learning effectiveness, loss of learning objective, and loss of learning direction may weaken the capability of an OLI/LS. A study conducted by Paolucci (1999) indicated that a relation do exist between the structuring of the knowledge domain and positive learning performance. More recently, Hoogveld, Paas, Jochems, & van Merriënboer (2001) pointed out that instructional design systems-based training produced a better training result in comparison with the experienced-based design condition in their empirical investigation. Consequently, a structure with respect to the arrangement of knowledge elements that deals with the instructional design problem becomes necessary in the OLI/LS development life cycle, and thus is the motivation of this research.
Eventually, the goal of an OLI/LS is to help learners obtain the whole domain knowledge rather than pieces of knowledge. In order to improve the capability of an OLI/LS, researchers pay increased attention to instructor modeling that deals with defining knowledge elements, ordering knowledge elements, grouping knowledge elements, and appropriately directing learning paths (Jonassen and Hannum, 1995, Hackbarth, 1996, Newby et al., 1996, Ferry et al., 1998). Therefore, a structure for knowledge elements becomes essential due to the requirements of organizing the massive amount of learning materials. Gordon (2000) suggested that creating a knowledge structure needs to identify specific pieces of knowledge and consider their relationships. Major techniques that can be used in the process of instructional design include selecting and sequencing (Landa, 1983, Gagne, 1985, Scandura, 1987, Jones et al., 1992, Hackbarth, 1996, Mengel and Adams, 1996). Particularly, the sequence of knowledge elements for a subject is often implied by the nature of knowledge in the element description. However, it is more than simply a description of the sequence in which the knowledge is performed (Jonassen & Hannum, 1995). The sequence for performing the knowledge implies an appropriate instructional progression. Consequently, the representations of materials have to be leveled by defining the interrelations among the connections. For example, knowledge of relational integrity rules in the subject of database management systems has to be covered before normalization is given. Therefore, the problem of ordering the conceptual links in sequence becomes critical while building the knowledge structure for an OLI/LS.
The concepts of breadth-first algorithm and depth-first algorithm that are widely used in the application of the artificial intelligence realm are utilized to organize the links of state spaces (Luger & Styblefield, 1993). The breadth-first algorithm uses a strategy that explores the space in the level-by-level fashion while depth-first goes deeper into the search space whenever it is possible. These algorithms are eventually used to determine the order where states are examined in a tree or graph. Therefore, the knowledge structure is basically formed before these algorithms can be performed. On the contrary, instructional design deals mainly with the formation of a knowledge structure for the knowledge elements defined. The Depth-First Structures (DFSTRUCTURE) and Breadth-First Structures (BFSTRUCTURE) that are used to help construct knowledge elements are employed. Any two of the knowledge elements with relations of dependency can form a DFSTRUCTURE. A BFSTRUCTURE forms if no relation for two elements is defined. The entire knowledge structure is built via the integration of BFSTRUCTURE and DFSTRUCTURE. The study presented in this paper is to develop a Knowledge Construction Model (KCM) that can help efficiently build a knowledge structure that guides a beginner to appropriately capture domain knowledge on a level-by-level basis.
The remainder of this paper is organized as follows. The DFSTRUCTURE and BFSTRUCTURE are described in Section 2. Section 3 presents the developed KCM where the user interface, knowledge table, model mechanism, and update facility are included. A practical application case used to demonstrate the utilization of KCM for the subject of database management systems is delineated in Section 4. Section 5 addresses the research conclusion.
Section snippets
DFSTRUCTURE and BFSTRUCTURE for a knowledge structure
The BFSTRUCTURE is based upon the principle of breadth-first search while DFSTRUCTURE from depth-first search. In breadth-first search, only when there are no more states (or nodes) to be explored at a given level does the algorithm move on to the next. Only when no further descendants of a state can be found are its siblings considered in depth-first search [18]. For example, if the breadth-first search is performed for Fig. 1, the search order is KE1, KE2, KE3, KE4, KE5, KE6, KE7, KE8, KE9, KE
The developed KCM
The operational process of knowledge construction is illustrated in Fig. 2. An instructor defines a subject (or a curriculum), decomposes the defined subject into a number of knowledge elements and defines their relationships, and organizes the knowledge structure that is formed with nodes (knowledge elements) and arcs (sequences). The KCM is developed with the capability of obtaining knowledge elements, confirming relationships of knowledge elements, and organizing and illustrating knowledge
A practical application case
The developed KCM has been employing to generate knowledge structures of online courses for instructors in the department of information management, Kun Shan University of Technology, Taiwan. While the developed model can be used in many domains, a subject of Data Base Management System (DBMS) was used as a practical application case to demonstrate its use. The procedure to build the knowledge structure contained three stages as described in the previous sections. They included decomposition of
Conclusion
As the Internet becomes increasingly important to the information delivery, so is OLI/LS used in distance education. The Internet technology brings learners plentiful materials from all over the world. However, a learner who does not have a relevant degree of understanding may not be able to know where to effectively and efficiently obtain the whole domain knowledge that could be integrated with the knowledge he/she already has. The KCM developed in this research is described in detail to help
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