2. A Proposal to Refine Concept Mapping
For Effective Science Learning
Meena Kharatmal & Nagarjuna G.
{meena, nagarjun}@hbcse.tifr.res.in
Homi Bhabha Centre for Science Education
TIFR, Mumbai, India
September 5, 2006
CMC2006, San Jose, Costa Rica

3. outline
concept maps in science education
●
point out problems in concept maps for
●
learning
point out problems in concept maps for
●
evaluation of concept maps
propose some refinements in concept maps
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propose an assessment model based on
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refinements in the concept map

4. concept maps
two dimensional graphical representation of
●
one's knowledge of a domain (Novak & Gowin,
1984)
based on Ausubel's theory of classroom learning
●
(Ausubel, et.al., 1978)
constructed using concepts, linking words,
●
branching, hierarchy, cross­links, examples
progressive differentiation,
incorporate
●
subsumption, integrative reconciliation (Mintzes,
et. al. 1998)

5. mainly used for knowledge elicitation
●
used in research studies for meaningful learning
●
review of ~150 studies on concept mapping:
–
concept maps helps students gain meaningful
learning, enhance the integration and retention of
knowledge (Mintzes et.al. 1997)
comparing successive concept maps: conceptual
–
change in a group of biology students as they
gained mastery of the domain (Carey 1986;
Wallace & Mintzes 1990) ­­­ use of more number of
critical concepts and propositions, more intricate
hierarchical structure, branching patterns, and
occurrence of cross­linkages

6. assumptions
to understand is to establish relations
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to educate is to help organize concepts
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learning involves restructuring i.e. conceptual
●
change
misunderstanding is due to incorrect
●
organization of concepts
science cannot be ambiguous, inconsistent,
➔
illogical
scientific knowledge (representation) must be
➔
explicit

7. Comparing the knowledge profile
of a novice and an expert
Profile of Novice Profile of Expert
Knowledge loose form, uneconomical, cohesive, integrated, parsimony,
Structure ambiguous relations unambiguous relations
Knowledge periphery core concepts
Organization
Approach superficial principled, accurate, deep
Theories concrete, fragmentary, abstract, global, consistent,
inconsistent, particular, diffuse universal, precise
Reasoning implicit and intuitive explicit and articulate
Brewer & Samarapungawan, (1991)

8. Concept map on “life in the ocean”
Martin, Mintzes, Clavijo (IJSE, 2000)

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

10. Traditional Concept Map Refined Concept Map
(using several linking words) (using minimum i.e. 5 relation types)
consists of / consists mainly of consists of
●
can be classified as includes
●
of the ocean are live in (habitat)
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aspects are live as (habit)
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including produces
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like
●
which are
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creates
●
unambiguous, precise,
includes 4 orders parsimoniously used relation types
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can be either / are either
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wide variety of relation types
has 2 groups / have 3 groups / has 3 (but not many)
●
classes / includes 4 orders / include
different dimensions
phyla / have 3 types

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

12. Hierarchy
... the number of valid hierarchies in the most branched
segment of the map to be counted (Novak & Gowin, 1984,
p. 107)
Hierarchies are scored based on the levels
●
Graphical representation of the levels does not
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follow from the logical definition of hierarchy
One has to validate the hierarchy:
logical criteria ­­­ must use the same relation type
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(Mayr, Cruse, Lyons, etc.)
hierarchy is the logical criteria of knowledge
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organization

13. Example
Novak, & Gowin (1984): Learning how to
learn, Cambridge University Press. (p. 16)

14. Example

15. Example
Refined
Living things
Includes
Plants Animals
e.g. e.g.
My dog
An oak

16. Attributes
Mintzes, et.al. IJSE (2002), p. 653

17. Attributes

18. Size
Attributes Shark
Size small
Teeth
Part of
medium
large
types/includes
Live in Used for Size
Fins
Reef
Food chain
Bottom of ocean Salt water Eat
Research
Tiger shark Great white Sand shark
Blue shark Whale shark Hammerhead shark
Attribute Types Attribute Values
Size Small
Medium
Large

19. Cross­links
Mintzes, et.al. IJSE (2002), p. 653

20. Critique of concept maps
ambiguity in linking words, lack of rigor in
●
concept mapping (Costa et. al. 2004)
non­rigourous in methodology (Kremer 1995;
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Sowa 1984, 2006)
lack of knowledge representation (KR) methods
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(Canas & Carvalho 2004)
...
●

21. Refinements
using a minimal set of linking words (relation
●
types) to represent large number of concepts
focus on: nature of linking words (relation
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types)
focus on: logical criteria while assigning
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hierarchy
distinction between monadic relation
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(attribution) and diadic relation (proposition)
scientific knowledge must be logical, consistent
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transformation from implicit to explicit
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22. Observations
We found that:
linking words are chosen from a set of NL and
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hence results in ambiguity
CYC ­­­ organizing common sense knowledge
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GO, MBO ­­­ organizing biological database using
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is­a relation
Science Education ­­­ 67 relations used to link
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about 2,300 concepts (Fisher, 1990)
Biology Education ­­­ 6 relation types used to
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link about 75 concepts to create a refined
concept map of a biology chapter (Kharatmal,
2006)

23. Proposed assessment model of using
refined concept mapping techqnique

26. to sum up
expert's depict knowledge structure using
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unambiguous, consistent, parsimonious nature
expertise can be achieved when the characteristics
●
mentioned above are implemented
if during the course of learning a novice (student) is to
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be transformed into an expert then it is essential that
the novices are trained to organize their knowledge like
an expert does

27. References
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●
Winston.
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●
College Science Teaching, 117—121.
Canas, A. J. and Carvalho, M. (2004). Concept maps and AI: An unlikely marriage? In Proceedings of SBIE 2004: Simp sio
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Brasileiro de Inform tica na Educa o, Manaus, Brasil. Simp sio Brasileiro de Inform tica na Educa o, Manaus, Brasil.
http://www.ihmc.us/users/acanas/Publications/ConceptMapsAI/Canas­CmapsAI­Sbie2004.pdf.
Carey, S. (1986). Conceptual change and science education. American Psychologist, 41(10), 1123­­1130.
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Costa, J. V., Rocha, F. E., & Favero, E. L. (2004). Linking phrases in concept maps: A study on the nature of inclusivity. In A. J.
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Canas, J. D. Novak, & F. M. Gonzalez (Eds.), Concept Maps: Theory, Methodology, Technology. Universidad Publica de
Navarra, Pampalona, Spain.
Fisher, K. (1990). Semantic networking: The new kid on the block. Journal of Research in Science Teaching, 27(10), 1001­­
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IHMC CmapTools (2004). The Website of CmapTools. http://cmap.ihmc.us
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Kharatmal, M. (2006). Concept map on cell structure and function. At IHMC Public Cmaps Meena (India) Cell Structure and
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29. Thankyou
meena@hbcse.tifr.res.in
www.hbcse.tifr.res.in/~meena
www.gnowledge.org/