Development of a Trans-Field Learning System Based on Multidimensional Topic Maps
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Development of a Trans-Field Learning System Based on Multidimensional Topic Maps

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A knowledge base consisting of topic maps of various fields, such as physics, chemistry, earth science, industry, daily life, history etc., was constructed for an online learning system. Topics in......

A knowledge base consisting of topic maps of various fields, such as physics, chemistry, earth science, industry, daily life, history etc., was constructed for an online learning system. Topics in different fields were interlinked by using specific inter-field associations. Also, topics that were cited in more than one field were put in the field where they are most central and were also linked in the contexts of each of the other field. This indicates the multidimensionality of topics linked with multiple topic maps. In addition to the main knowledge base, two types of topic map with smaller granularity were created for test cases. The first was for the expression of variant definitions of basic quantities for various grade levels, and the second was for generating instruction scenarios. These micro-topic maps were linked to the main knowledge base map by attaching the same subject identifiers to corresponding topics in both maps.

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  • 1. Development of a Trans-Field Learning System Based on Multidimensional Topic Maps Shu Matsuura Tokyo Gakugei University, Faculty of Education
  • 2. An online learning system: “Everyday Physics on Web” supported by Naito-san.
    • To associate knowledge in a wide variety of fields subject: Physics, Chemistry, Biology, Earth Sicence, Astronomy, Environment, Sustainability, Industory, Aritifact, Daily Life, Policy, History of Science,,,
    • For informal learning and for self-learning ( of students, teacher,,,).
    • Topic Maps application server: Ontopia Navigator Framework at http://tm.u-gakugei.ac.jp:8080/epw/ Thanks to open-source Ontopia.
  • 3. Photo topic link with http://psi.garshol.priv.no/tmphoto/ by TMRAP. This site started on Sep. 2009.
  • 4. Change from Course-centric to Subject-centric Course-centric portal for online learning (<2007) - easy to start course learning - fragmented knowledge     - restrict the range of learning Subject-centric, association-centric portal One can start with any topic, and recorded and evaluated. (<2009) Topic map based trans-field learning portal for informal learning.
  • 5. Distributed knowledge is_based_on association Sequential learning preceding_following association Associations of knowledge is not in a straight line. 1. Avoid fragmentation of knowledge position and displacement velocity acceleration inertia a = f/m momentum action, reaction normal force friction work position and displacement velocity acceleration inertia a = f/m action, reaction normal force friction work
  • 6. Course-centric LMS tends to restrict the range of study. Topic maps-driven learning system will be appropriate to free-style self-learning. “ Courses” are embedded into the association “preceeding_following”. “ Subjects” are embedded in the course. Course Learning Resource Learning Record Course-centric Learning Management System
    • learning order assoc.
    • intra-field assoc.
    • inter-field assoc.
    Our Topic Maps-Driven Portal
  • 7. Radar chart of number of learning records on 5 fields of physics for individual learner z 方向 “ a learning vector” : L =  n i e ri + N e z An index a for the anisotropy of learning found in the radar chart. a = |  n i e ri | / N e ri is the unit vector of i’th field n i is the number of learning records on i’th field
  • 8. Anisotropy of learning vs. amount of request of individuals aniostropy index a a = |  n i e ri | / N N a Filled black circle ●: course-centric portal used.
    • Students who have large amount of request showed broader range of the anisotropy.
    • Students who have small amount of request showed relatively high anisotropy.
    using topic maps portal (○, △, □) : -> Variation in the ways of learning appeared through the repetition of study.
  • 9. Text on “force” Text learning record on “force” is_subject of_ResourceText is_subject of_TextLearningRecord “ Force” subject subject a subject b is_based on A Portal for
  • 10. Fields : Physics, Chemistry, Biology, Earth Science, Astronomy environment, sustainability, daily life, history, policy, history Field Subject δ Learning Resource layer Learning Record layer subject a1 subject a2 subject b1 subject b2 sub-field a Subject Space Field Subject α Field Subject γ sub-field b Field Subject β Inter-Field Subject Association
  • 11. Taxonomy of topic types. Tracing up and down the hierarchy, one can find out sub-domains to explore.
  • 12. Types of associations inside the fields look to reflect the characteristics of the field. Intra- and Inter-field subject association. Tracing the associations between topic instances is another way to explore subjects.
  • 13.  
  • 14. subject a1 a3 a2 a4 subject b1 b2 b3 b4 subject c1 c4 c3 c2 Field α Field β Field γ trans-field associations retrieved topic Image of a possible visualized interface for trans-field association.
  • 15. Our present page for an instance topic: type hierarchy structure + associated topics & their occurrences. At present:
  • 16. An example of topic instance page Google Earth, Map & YouTube as occurrences of the topic instance.
  • 17. Light scattering Mie theory Tyndal 現象 Dispersed system Properties of matter Colloidal phenomena Atmospheric science Atmospheric optics Crepuscular ray Physics Chemistry Earth Science
    • “ Tyndal effect” is positioned in three context coordinates.
    • Due to the consistency of topics, topic instance cannot be duplicated.
    • Knowledge of Tyndal effect will be refined through understanding in the multiple fields.
    Tyndal 現象 Tyndal effect A shared topic
  • 18. physics axis chemistry axis earth science axis Light scattering Mie theory Dispersed system Colloidal phenomena Atmospheric science Atmospheric optics Crepuscular ray Tyndal effec t Tyndal effect Tyndal effect Tyndal effect A multidimensional representation of a shared topic
  • 19. subject a1 a3 a2 a4 subject a1 b2 b3 b2 subject a1 c4 c3 c2 Field α Field β Field γ subject a1 subject a1 A topic shared by 3 fields A shared topic is located at several corresponding fields.
  • 20. a3 a2 a4 Field α Field β Field γ An image of possible visualized interface for multidimensional association. “ covalent bond”? subject a1 b2 b3 b2 subject a1 c4 c3 c2 subject a1
  • 21. a3 a2 a4 Field α Field β Field γ Making fine structure around a topic, by adding a micro topic map a “micro” topic map that has common topics with main map. Connect a micro topic map to the main map in the application (not merging maps). a3 a2 subject a1 b2 b3 b2 subject a1 c4 c3 c2 subject a1
  • 22. Work Elementary Def. of Work Scalar Force Distance Fundamental Def. of Work Generalized Def. of Work Is defined by Is a function of Vector Force Displacement multiplication Scalar Product Integral uses operation of A micro topic map of the definition of “Work” at three levels of generalization.
  • 23. Instruction Scenario Introductive Experiment Questions Experimental Evidence Understanding precedes Question topics Basic Physics Subject Basic Chemistry Subject Daily Life Subject Physics Experiment Scenario Type topics An example of instruction scenario topic map with hands-on experiments. Knowledge topics and experiment topics are applied to a scenario type.
  • 24.
    • Multiple-subject topic maps for science and technology fields were introduced, and trans-subject associations were used to interlink topics in different fields.
    • We discussed a multidimensional character of this topic map system.
    • Two types of micro-topic maps were created and linked with the main multidimensional map by using identical subject identifiers for common topics.
    • It was suggested that topic maps for various purposes could be created flexibly, using the main multi-field subject topic map as a knowledge base.
    Concluding remarks.