1. Knowledge Organizers of Cell Biology
Meena Kharatmal & Nagarjuna G.
{meena, nagarjun}@hbcse.tifr.res.in
EPISTEME ­1
December 16, 2004
Homi Bhabha Centre for Science Education
(Tata Institute of Fundamental Research)
Mumbai, INDIA

2. Working Hypotheses of Knowledge Organization in
Science Education
To understand is to establish relations between concepts
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To educate is to help to organize concepts
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All learning involves restructuring (conceptual change).
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Misunderstanding is due to incorrect organization of
●
concepts
Goal of teaching is to restructure (reorganize) novice's
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knowledge structure so as to align with expert's
knowledge structure

3. Importance of Knowledge Organization in Science Education
Understanding of knowledge organization (KO) will help in
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building a framework for curriculum development
To understand the transformation (conceptual change) of
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novice into an expert
Curriculum designed using KO approach follows a principled
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approach, which is used by the experts in their respective
domains. Incorporating the principled/logical approach is
very essential to transform a novice into an expert (which is
the goal of education)

4. Knowledge Representation (KR)
A KR is a surrogate: KR acts as a surrogate for representing the physical objects,
●
events, relationships which cannot be stored directly in a computer. It can be
represented by symbols, and these symbols serve as surrogates for external system. A
computational model is a surrogate for some real or hypothetical system.
A KR is a set of ontological commitments: An ontology is a study of existence. A
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KB is determined by its ontology. Every KB is based on some conceptualization. An
explicit specification of this conceptualization is called an ontology. In ontology, what
one does is articulate the knowledge in terms of concepts, relations, axioms,
instances.
A KR is a fragmentary theory of intelligent reasoning: It serves to support
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reasoning about things in a domain.
A KR is a medium for efficient computation: It processes knowledge efficiently
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for problem solving on the available computing equipment.
A KR is a medium of human expression: A good KR language should facilitate
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communication between the knowledge engineers who understand AI and the
domain experts who understand the application. Although the knowledge engineer
may write the definition, and rules, the domain experts should be able to read them
Davis, Schrobe, Szolovits (1993); Sowa (2000)
and verify whether they represent a realistic theory of the domain.

5. Different ways of knowledge representation
Concept
Map
(Novak)
Conceptual Graphs (Peirce, Sowa)
Concept
Circle
Diagram
(Wandersee)
Semantic
Network
(Fisher)

6. How does KR help student learn?
A collaborative task occurs on the discussions about the meanings of
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concepts and the relations between them. The act of creating an organized
structure of ideas on paper or on a computer helps in creating a knowledge
structure in the mind.
KR helps in making the implicit (often fuzzy) knowledge into an explicit
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and precise knowledge. It incorporates cognitive and metacognitive skills,
thus occurs meaning­making.
KR helps students to make finer discriminations between ideas and helps to
●
organize better. The more one practices the better one becomes at organizing
and relating concepts.
Structural (organized, semantic) knowledge is essential to assimilate, recall
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and comprehend. Structural knowledge is essential to problem solving.
There exists significant differences between the structural knowledge of
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novices and experts, and hence for novices a natural part of learning is to
work on their structural knowledge to make it more expert­like.
Fisher (1996)

7. Comparing expert's and novice's knowledge structure
Expert Novice
Knowledge cohesive, integrated loose form
structure unambiguous relations ambiguous relations
parsimony uneconomical
Knowledge core concepts periphery
organization
Approach principled, accurate, deep superficial
Theories abstract, global, consistent, concrete,
fragmentary,
universal, precise inconsitent,
particular, diffuse
Reasoning explicit and articulate implicit and intuitive
Brewer, Samarapungawan (1991)

8. Methodology
Classify concepts on the basis of their cognitive function
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Assign valid and authentic semantic relations to the concepts
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Analysis of the knowledge­base based on the usage of
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different kinds of semantic relations applied
Comparing the novice's knowledge structure with that of an
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expert's knowledge structure
Restructuring (reorganizing) to align the novice's knowledge
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structure with the expert's knowledge structure
Develop a minimal set of relation types for representing the
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entire domain of biology

9. 3­layer model of GNOWSYS
MetaType MTRelation
MetaType
MetaType
layer
MetaType
MetaType
MetaType MetaType
MetaType
Instance of MetaType
Type
AT AT
layer AT
ObjectType ObjectType
RelationType
AT
Instance of Type
Token
A A
layer A
Object Object
Relation A

10. Classify concepts on the basis of their cognitive function
MetaType
Taxonomical concept Structural concept Process concept
Relational concept
Kingdom TYPE
Spatial inclusion
Phylum Meronymic inlucsion
Class inclusion
Class
Instance of
ObjectType
Structure Process
Animalia Cell Protein synthesis TYPE
Cytoplasm
Vertebrata
Nucleus Lipid synthesis
Mammalia
Nuclear envelope
Human Diffusion
Mitochondria
Fish Metabolism
Ribosomes
by
Instance of
ed
Object
in
co
Robert Hooke
TOKEN

11. Assign valid and authentic semantic relations to the concepts
Meta Type
Relational concept Class inclusion
Meronymic inclusion
Spatial inclusion Functional
RelationType
Ribosomes located on endoplasmic reticulum
Vertebrata includes fish, mammal
Protein synthesis occurs in cytoplasm
Mitochondria, ER, part of cell
Amino acids includes alanine, glutamine
DNA wound around histones
Purines includes adenine, guanine
Ribosomes located on ER
Nucleus function DNA synthesis
Plankton includes phytoplankton, zooplankton
Relation
Schleiden, Shwann formulated Cell theory

12. Classify concepts on the basis of their cognitive function
cell small lipid synthesis
nucleus frog osmosis
cytoplasm nekton diffusion
endoplasmic reticulum plankton metabolism
mitochondria ocean my cell
protein synthesis size 20nm
animalia eukaryotic cell ribosomes
mammalia kingdom shark
whale phylum 1µ m
structure
Watson class
process
Hooke vertebrata attributes
objects

13. Assign valid and authentic semantic relations to the concepts
Animalia instance­of kingdom class membership
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Vertebrata instance­of phylum
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class inclusion
Vertebrate includes mammalia, fish
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meronymic inclusion
Shark instance­of chondrichthyes
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Kingdom subtype­of taxonomical concept spatial inclusion
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Mitochondria part­of cell
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Ribosomes part­of cell
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Instance­of
Robert Hooke instance of human
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subtype­of
Nuclear envelop surrounds nucleus
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part­of
surrounds

14. Semantic relations
Meronymic inclusion (part­of/consists­of)
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Nucleus, cytoplasm, ER, mitochondria part­of cell
–
Phosphoric acid, pentose sugar, nitrogenous base part­of nucleotides
–
Amino acids part­of proteins
–
Class inclusion (includes /type­of, subclass­of)
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Amino acids includes alanine, glutamine
–
Purines includes adenine, guanine
–
Vertebrates includes mammals, fish
–
Spatial inclusion (surrounded by/surrounds)
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Nucleus surrounded­by nuclear envelope
–
DNA wound around histones
–
Ribosomes located on endoplasmic reticulum
–
Functional (function)
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Smooth ER function lipid synthesis
–
Golgi apparatus function transport of materials
–

15. Process modelling
Object undergoes event
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Object transforms into object
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Event takes place in region
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Event takes place during time
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16. Process modelling for prophase
Structure Process
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Chromatin undergoes condensation;
Condensation takes place in nucleus
Chromatin Condensation
– –
Chromatin transforms into chromosome
Chromatin moves towards nuclear envelope
Chromosome Reduction
Nucleoli reduces in size – –
Reduction takes place in nucleoplasm
Nuclear envelope undergoes fragmentation
Nucleoli Movement
– –
Fragmentation results in disappearance
Spindle formation takes place in cytoplasm
Nuclear envelope Fragmentation
– –
Centrioles undergoes movement
Spindle undergoes lengthening
Spindle Formation
– –
Centrioles
Chromatin –
Condensation
Location/Region
Effect ●
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Nucleus Nucleus
Disappear –
–
Chromosomes Nucleoplasm
Lengthen –
–

17. Concept map on “life in the ocean”
Many relation types
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1st level
Hierarchy not ordered
2nd level ●
3rd level
Hierarchy not validated
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4th level
Incorrect cross­links
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5th level Graphical representation
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misleading
6th level
Not principled
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Martin, Mintzes, Clavijo (IJSE, 2000)

18. Principled concept map on “life in the ocean”
Consists Ocean
of
Includes
Living Beings Non­living Beings
Habi (Biotic) (Abiotic)
t
Habita
t Animals Plants
Produce
s
Geological
Chemical
Physical
Seagrass Algae
Plankton Pleuston Nekton Vertebrates
Invertebrates
Cnidaria
Chlorophyta
Fish Mammal
Phytoplankton Arthropoda Current
Wave
Phaeophyta
Porifera Wind
Zooplankton
Rhodophyta Crustal plate
Mollusca
Inorganic Organic boundaries
Agnatha
Osteichthyes Carnivora Pinnipeda
Holoplankton Chonodrichthyes Cetacea Sirenia
Meroplankton Ca Cl
Ligands
K
Na
Co3
Mysteceti Odonteceti Constructive
Rays
Shark
Conservative
Destructive

19. Principled concept map on “organic molecules”
Consists of
Organic molecules
Includes
Polysaccharides Proteins Lipids
Nucleic acids
Minimal Set of RNA
Knowledge
DNA
Organizers
Fatty acids
Monosaccharides Amino acids Nucleotides Glycerol
Principled Concept
Alanine
Map:
Glutamine
Concepts
➔
Glycine
Relation types
Phenylalanine Pentose sugar
➔
R
H C
Relations
➔
Phosphoric acid Nitrogenous bases
NH2
COOH
Hierarchy
➔
Branching
➔
Cross­links
Purines Pyrimidines
➔
Instances
➔
Attributes
➔
Cytosine
Metatypes
➔
Adenine Guanine Thymine Uracil

20. Screenshot of
GNOWSYS

21. Screenshot of
GNOWSYS
Knowledge
Organizers

22. Screenshot of
GNOWSYS
Meronymic
Inclusion

23. Screenshot of
GNOWSYS

24. Screenshot of
GNOWSYS
Relation Process
Structure

25. Implications of the reasearch
Study the transformation and restructure (reorganize) the
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novice's knowledge structure with that of an expert's knowledge
structure
To develop a knowledge base of biological concepts with valid
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and authentic semantic relations (can serve as criteria maps for
assessment and scaling)
To develop a principled concept mapping approach with scaling
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and assessment criteria using the knowledge base
Develop a controlled language (small subset of natural
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language) to express scientific knowledge based on minimal
knowledge organizers (following the concept graphs of Peirce
and Sowa)

26. Ausubel, Novak and Hanesian (1978): Cognitive Physchology: A Cognitive View, Holt, Rinehart and Winston, New York
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Brewer and Samarapungavan(1991): Children's Theories vs. Scienctific Theories: Differences in Reasoning or Differences in Knowledge? In
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Hoffman and Palermo (Eds.), Cognition and the Symbolic Processes: Applied and Ecological Perspectives, pp. 209­232, Erlbaum, NJ.
Carey (1986): Conceptual Change and Science Education, American Psychologist, 41(10, pp.1123­1130.
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Castro, Peter and Huber, Michael (2003): Marine Biology, Fourth edition, McGrawHill, USA.
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Fisher, Wandersee and Moody (2000): Mapping Biology Knowledge, Kluwer Academic Publishers, The Netherlands
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Fisher and Kibby (1996): Knowledge Acquisition, Organization and Use in Biology, Springer­Verlag, Germany
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Grabowski (2000): Principles of Anatomy and Physiology, John Wiley and Sons, New York
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International Journal of Science Education: 2000, 2002
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Journal of Research in Science Teaching: 1990, 1994, 1996, 2000
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Mader (2000): Inquiry into Life, McGraw Hill, USA
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Mintzes, Wandersee and Novak (1998): Teaching Science for Understanding ­­­ A Human Constructivist View, Academic Press, USA
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Mintzes, Wandersee and Novak (2000): Assessing Science Understanding ­­­ A Human Constructivist View, Academic Press, USA
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Novak, Gowin (1984): Learning How to Learn, Cambridge University Press, UK
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Soper (1997): Biological Science, Cambridge University Press, UK
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Sowa (2000): Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole, USA
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Sowa (1984): Conceptual Structures: Information Processing in Mind and Machine, Addison­Wesley Publishing Company, USA.
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Stephens and Chen (1995): Principles for Organizing Semantic Relations in Large Knowledge Bases, IEEE.
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Storey (1993): Understanding Semantic Relationships, VLDB, 2, pp.455­488
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Winston, Chaffin and Hermann (1987): A Taxonomy of Part­Whole Relations, Cognitive Science, 11, pp. 417—444
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