Building a System Which Automatically Externalizes Teachers' Instructional Design Intentions and an Evaluation of Its Effectiveness

• Kasai Toshinobu

  Graduate School of Education Master's Program, Okayama University

• Nagano Kazuo

  Faculty of Liveral Arts, the University of the Sacred Heart

• Mizoguchi Riichiro

  Japan Advanced Institute of Science and Technology

Bibliographic Information

Other Title

• 教師の授業設計意図自動外化システムの開発とその有効性評価
• Building a System Which Automatically Externalizes Teachers' Instructional Design Intentions and an Evaluation of Its Effectiveness

Abstract

In order to facilitate learners' knowledge refinement process, it is effective to let them externalize their knowledge. However, in a domain of the instructional design in which existence of knowledge and its necessity are not sufficiently articulated or recognized, it is not easy for teachers who are also learners of how to externalize their knowledge. In this study, we have built a system called ``FIMA-Light'' which uncovers knowledge that teachers must have applied in their lesson plans from global to local viewpoints instead of them. FIMA-Light makes use of the OMNIBUS ontology which describes various instructional knowledge for attaining educational goals extracted from instructional/ learning theories. And, FIMA-Light automatically generates what we call I_L event decomposition trees by interpreting a given lesson plan based on the OMNIBUS ontology. Then, FIMA-Light facilitates teachers' deep reflection and helps them to refine their lesson plans by providing them with decomposition trees. We report some results of an experiment carried out for evaluation of the quality and the effectiveness of I_L event decomposition trees.

Journal

• Transactions of the Japanese Society for Artificial Intelligence

  Transactions of the Japanese Society for Artificial Intelligence 30 (3), 570-584, 2015

  The Japanese Society for Artificial Intelligence

Keywords

• ontology
• instructional design
• externalizing knowledge
• reflection

Details

• CRID: 1390282680084061440
• NII Article ID: 130005068485
• DOI: 10.1527/tjsai.30.570
• ISSN: 13468030
• ISSN: 13460714
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed