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Simply put, for example, a behaviorist way of instruction is modeled so that the goal of helping learners learn would be decomposed into a sequence of actions like 1 giving stimuli, 2 observing the response, 3 repeating, and the same goal would be decomposed into 1 preparing an environment, 2 putting learners in it, and 3 letting them build knowledge, if the constructivist approach is adopted.

Therefore, computers can choose the best way of decomposing a given goal among possible ways extracted by analyzing theories and stored in OMNIBUS ontology. OMNIBUS has the framework to include all theories and models, provided that they comply with the specification of the concepts of theory and of model. In this sense, it is the first and, to our knowledge, the only authoring system that does not constrain or favor one theory over another. The author, a person is making decisions to select the appropriate theories, which are complex and high-level decisions. It should be considered as a next-generation intelligent system running completely on the basis of an ontology without any heuristic knowledge.

At every decision point, it can provide authors with available ways of decomposing a given goal into sub-goals by referring to decomposing ways suggested by the theories stored in OMNIBUS. By repeating such decomposition operations, authors can obtain a tree of sub-goals all of whose decompositions are supported by theories. This proposal was very instrumental in solving the problem of representing learning and instruction separately.

We realized that we were unable to find all the concepts that we needed to build the ontology.

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This is why we created new concepts, out of necessity. As you know, we first tried to find a solution to our problem in Task Ontology, and were not successful. We had little progress for years. Then, I stopped our plan and tried to find one from another perspective or by another way. And, I suddenly found my idea of function decomposition Kitamura et al. The heart of functional decomposition is the interpretation of any function as a goal-oriented interpretation of state change of before and after performance of the function.

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So, state is the key issue. I tried to find states in our domain. But it is not the only one in our domain.


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State used in function decomposition must be singular, I mean, a unified state. But, instruction and learning actions are different and each has its own resulting state Oh yes, even if instruction and learning are different actions done by different agents, they can be thought to form a unified pair like you speak and I listen, you write to me and I read it, What we needed is to express any knowledge, competency, skill that someone is learning, intentionally or not, and with or without instruction.

Again, although it is a correct concept, the term that we chose could be improved. In computer science, an ontology is the specification of conceptualization, with properties and relations, based on the study of existence. Strangely enough, in our efforts to represent existence, we had to create a new existing thing concept in order to represent reality. Viewed from an ontological perspective, ways of decomposition are entirely supported by concepts defined in the OMNIBUS ontology, that is, there are no ad-hoc concepts or knowledge in them.

One activity is the use of SMARTIES with a help of the first author of Hayashi and Mizoguchi in Tochusha an official community of junior high school teachers in Tokyo for promoting teaching knowledge sharing and development in the subject of social science. The new system is named FIMA-light and has been built using rule base technology. The performance was surprising. Another feature of FIMA-light is that it is domain-independent.

In fact, it has been positively assessed using lesson plans of five different subjects made by four junior high school teachers. From the methodological viewpoint, a significant evolution occurred during these years, and is summarized in this section. The first version of our OE methodology Mizoguchi et al. With YAMATO, Mizoguchi established the universal and fundamental concepts that would allow him and his team to unify and share a description of the existing: event, process, agent, etc. He then oriented the development of OMNIBUS in this direction, with the constraint of relating our ontology to an top-level ontology, whereby we ensured that the specification at the most abstract level of basic concepts such as event , process , system.

This ontology engineering principle has two advantages: on the design level, it provides a common vocabulary, and on the scientific level, it allows for refutation or discussion at the most abstract and even philosophical level. In this section, we review a number of research projects that happened either as a direct result of our work, or were directly influenced by it. Seiji Isotani and Mizoguchi built an ontology of learning theories for group formation by interpreting some theories from the view point of group formation Isotani et al.

At that time, group formation was mainly done in ad-hoc ways.

The proposed method opened a new direction of theory-justified group formation. The work is still evolving and the recent result is found in Isotani et al. In addition to the original features of AIMS, ATO enhanced it as an intelligent authoring system which can help authors build domain models, course models and instructional models in a unified manner. To illustrate this idea, she developed CIAO, a service to the instructional designer. Our work inspired Savard in her doctoral dissertation, where she explored ways to model culture and cultural variables to inform instructional design decisions Savard et al.

The Common Practices category consists of the variables: learning aims, lesson plan, rhythm of learning activities, learning situations, pedagogical communication, cooperation-collaboration, detailed feedback, summative evaluation methods, and the interpretation of results. Why would we need or wish to have an ontology? As a result, the TeLearn open archive was created, dedicated to research in the field of Technology-Enhanced Learning.

The Thesaurus was extracted from the corpus of scientific papers contained in TeLearn, and a list of terms was produced with their weight number of occurrences and the strength of their links. Under the class Enhancing mechanism are the mechanisms used to enhance learning. Knowledge representation is used to model the learner, the domain, or to represent other kinds of knowledge. Adaptation is used to adjust the level of problems, the epistemic feedback, the instructional strategy, or the kind of visualization.

The role of collaboration is to foster learner motivation and engagement, as well as to implement strategies and tools for social construction of knowledge.

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Mobility is a new way to enhance learning, and Simulation a way to enhance inquiry learning, as well as to pursue the study thereof. In our view, these technologies make sense when associated with an enhancing mechanism used by the researchers. The authors claim that when integrated with the TEL classes, it will become possible to finalize the ontology.

This effort illustrates the feasibility and the benefit to work both bottom-up and top-down to obtain a successful integrated domain ontology. Blanchard and Mizoguchi built an ontology of culture intended for developing culturally-aware ITSs Blanchard et al. Blanchard had initiated a series of International Workshops on Culturally-Aware Tutoring Systems 4 CATS since , where Bourdeau and Savard also participated on a yearly basis, while Blanchard and Mizoguchi continued to work on their ontology of culture. In this section, we summarized several research efforts in which our work has and clearly been instrumental.

Moreover, considering the number Google scholar citations as of August 25, , a large number of researchers have read the original IJAIED paper and either commented it or directly integrated it in their own work. In spite of the authors' efforts in demonstrating how to proceed in the suggested directions for the use of ontology engineering in AIED, research on ontology building has not reached our expectation.

Instead, vocabulary-oriented ontologies have been popular in the community and led to interesting activities around metadata in the Semantic Web SW including Learning Design LD. A good survey on this topic is found in Dietze et al. Both trends are fundamentally different from our approach and sometimes contrast with it.

An exception is the research reported in Sklavakis and Refanidis , where the authors discuss an ontology-driven ITSs in line with our idea of ontology engineering. This topic is well-discussed in Devedzic We often see a common conflict between two parties either of which is involved in the process of achieving the same goal, say, building a ideal system which necessarily has conflicting properties.

A typical example would be knowledge-based systems like expert systems and the SW systems where conflicts appears between high functionality quality vs. The former adopts a strategy to attain high functionality first putting scalability aside, while SW systems try to attain scalability first rather than pursuing high functionality.

Ontology building is not an exception. A good ontology is expected to possess high quality and scalability.

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By high quality, we here mean not only consistency but also fidelity to philosophical theories and to the target domain as its model. The difference is usually methodological or strategic for achieving the shared goal. One party would like to attain high functionality first in a limited domain, while the other aims to attain scalability as the primary property.

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The conflict in the ontology engineering community would be better interpreted as a methodological conflict between top-down and bottom-up approaches to building ontologies See Fig. The issue here is not the question of which party is bigger than the other.

Either way may be right. The real issue is two-fold: 1 we do not know the best way to proceed directly toward the goal and 2 neither way might be successful in reaching the goal. We know performing a top-down way requires us to overcome several difficulties and barriers.