An Ontological Approach to Online Instructional Design

An Ontological Approach to Online Instructional Design

Robert Z. Zheng (University of Utah, USA) and Laura B. Dahl (University of Utah, USA)
DOI: 10.4018/978-1-60566-782-9.ch001
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Abstract

This chapter introduces the ontological instructional design as an alternative to the traditional instructional design in teaching and learning. By comparing the differences between traditional instructional design and e-Learning, the authors suggest that instructional design in e-Learning require a different model than the existing traditional models due to the idiosyncratic nature of e-Learning in terms of population, environment, and resources. An ontological instructional design model is proposed with a focus on the sharability, reusability and interoperability of ontological entities and design components within the ontological entities, which provides a holistic approach to online instructional design compared to the segmented, linear design approach in traditional instructional design models. A case study is included to illustrate the use and application of the ontological instructional design model in an online business course. Finally, guidelines for implementing the model are made with suggestions for future research.
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Issues Of Applying Traditional Instructional Design Models To Online Learning

Studies over the last decade have focused on the issues related to the applicability of traditional instructional design models to e-Learning (Akbulut, 2007; Rutherford & Kerr, 2008). Research in this field has so far produced mixed results. Some believe that traditional instructional design models can be universally applied to any instruction, online or offline (Anglada, 2008; Bi, 2000). Others argue that traditional instructional design models may not fit e-Learning due to their rigidity and lack of flexibility in design (DeSchryver & Spiro, 2008; Gunawardena, Ortegano-Layne, & Carabajal, 2006; Koh & Branch, 2004). Crawford (2004) explored online learning and traditional instructional design and found that there were apparent discrepancies between the two models. According to Crawford, the e-Learning model allows for exploratory, constructivist concept building whereas the traditional instructional design model is procedure-centric which allows little room for creative learning. Consistent with Crawford’s finding, Barron, Orwig, Ivers, and Lilavois (2002) found mismatches between traditional design models and e-Learning models in terms of individualized learning, collaboration, instructional delivery, and instructional design.

Individualized Learning

There are significant differences regarding the theoretical assumptions of individualized learning between traditional design models and e-Learning models (Barron et al., 2002; Harris, 1998). Traditional design models assume that all learners must learn at exactly the same pace and at the same level of content mastery. As such, instructional goals and learning objectives in traditional instructional design are routinely set to fit the normal curve, with little concern for the individual outliers (Moller, Foshay, & Huett, 2008). The above theoretical assumptions and their resultant approaches in traditional instructional design can be problematic because it is difficult to fit online learners into this traditional normal curve. An online learning community is characterized by its diversity in terms of prior knowledge, learner characteristics, motivation, social and economic status, and so forth (Moller et al., 2008; Proske, Narciss, & Korndle, 2007). Therefore, designing online instruction based on normal curve practice and the assumptions of traditional design theory would adversely affect the online learning community where individualized support at various levels is needed.

Key Terms in this Chapter

Ontological Instructional Design: Ontological instructional design involves examining the relationships between ontological entities in a system and between ontological components within an ontological entity. The ontological design model is used to organize and make connections between various ontological entities in the system. Considerations must be given to whether the ontological entities facilitate learners’ experience in knowledge construction and sharing and whether the ontological entities operate on the rule of interoperability and sharability so that a network of knowledge domains can be created.

Epistemology: Epistemology or theory of knowledge is the branch of philosophy that studies the nature, methods, limitations, and validity of knowledge and belief. The early epistemology such as Descarte emphasized the first truths as a foundation to construct a picture of reality (Fuller, 1955). Differing from the classical ontology as represented by Plato and Socrates, Descarte’s epistemology challenged the established truths by questioning every belief that one holds about external reality. This view of beliefs was associated with the rise of science and the successes associated with the scientific method, which Descartes helped codify (Spector, 2001). An important focus in epistemology is to try to reach truth through empiricism - what we can know through our senses - which we can summarize as “we believe what we see”.

Knowledge Representation: Knowledge representation in ontological design consists of content structure and format. The content structure of knowledge representation is formulated based on the inputs from knowledge repository and design component repository. The format often takes the form of modules which have two different presentations, shells and carriers, the selection of which is dependent on the purpose of the instruction. For example, if the instruction is to deliver the content for learners to learn the basic concepts and skills, the carrier presentation will be used to deliver the content. If the purpose of the instruction is to develop skills in knowledge construction, the shell presentation will be used for learners to construct new knowledge. In short, the shells enable learners to create new knowledge and share it with other learners. The carriers store the knowledge which is presented to the learner.

Ontology: The term ontology essentially refers to the study of being or existence. It seeks to describe or posit the basic categories and relationships of being or existence to define entities and types of entities within its frame work. In Greek philosophy, ontology means understanding the eternal reality (Reginald, 1985). Plato defined the reality as primary and the perception and experience of reality as secondary. Socrates held the similar view that the purpose of learning was to recognize eternal truth and therefore the instruction was to remind someone of something already known and accepted as true (Spector, 2001). This classical view of ontology has changed due to a realization that the explication and uncovering of external reality involve human perceiver and that human judgment with respect to such uncovering is subject to error. One of the assumptions of modern ontology is that instruction should focus on bodies of knowledge rather than individual knowledge. Instead of relying on one-to-one correspondence between individual beliefs and external reality, the modern ontology proposes a coherence theory of truth in which acceptance or rejection of new beliefs should be based on how well new beliefs fit with and how coherent they are with the established beliefs (Quine & Ullian, 1978; Spector, 2001). This assumption is important in that it influences the formation of ontology in computer science in which the shared conceptualization of reality across knowledge domains is emphasized.

Design Component Repository: The design component repository consists of design components similar to those in traditional design models (Dick, Carey, & Carey, 2005; Gustafson & Branch, 1997; Smith & Ragan, 2005). It operates on the shared knowledge rule which determines the interactivity of the design components and the level of interfacing with the domains in knowledge repository.

Ontology in Computer Science: Derived from its philosophical origin, an ontology in computer science is a data model that represents a set of concepts within a domain and the relationships between those concepts. For example, in artificial intelligence, software engineering, biomedical informatics and information architecture, the ontology is defined as a form of knowledge representation about the world. Ontologies in computer science generally describe (1) Individuals: the basic or “ground level” objects; (2) Classes: sets, collections, or types of objects; (3) Attributes properties, features, characteristics, or parameters that objects can have and share; (4) Relations: ways that objects can be related to one another; and (5) Events: the changing of attributes or relations

Knowledge Repository: Knowledge repository refers to a wide range of knowledge domains across various subject areas including math, physics, biology, social science, language, etc. Domains within the knowledge depository are connected by semantic rules and can be accessed through domain identifiers and classes. Knowledge repository primarily interfaces with the design component depository in which the design components like goal analysis, task analysis, learner characteristics, and so forth interact with the knowledge domains to provide inputs for the design of knowledge representation. Since knowledge repository operates on semantic rules, the domains become sharable within the knowledge repository as well as with ontological entities outside the knowledge repository such as design component repository.

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