Katleen Fisher

Untitled

Enhancing cognitive skills for meaningful understanding of domain specific knowledge.

Concept mapping and Hyper Media

The Tao of Mapping

Laboratory Interactive Learning

Laura Novack

Work

Verbal Architecture:

learning representations students support education diagrams maps mapping "semantic networks" constructing

Summary:

Cartography provides a concise means for representing large volumes of data in ways that are visually interesting and comprehensible, and that reveal important patterns and trends.

We argue here that successful visual representation of large bodies of information is the key to success in the information and communication age.

Knowledge mapping has been utterly neglected, in contrast to the great strides made in other forms of cartography.

Most scientists and science education researchers are not enamored with the primitive forms of knowledge mapping currently available, and they have yet to recognize the need for powerful knowledge-mapping capabilities in the future.

We predict that, in the future, computers will create maps of existing text without human guidance-- -that is, will automatically generate knowledge- distillation products.

There is considerable research regarding knowledge mapping as a constructivist learning activity in which the external map provides a tangible arena for the manipulation and organization of ideas.

In the same decade but on a different continent, science educator Joseph Novak and his graduate students invented concept mapping as a learning tool for K--12 students (Stewart, Van Kirk, & Rowell, 1979).

Dr. Robert Abrams at the University of California---Santa Cruz, developer of the LifeMap software, has been working with Fisher to develop a prototype for concept map-Gowin's Vee- SemNet conversions.

Reading a well-designed map is obviously a lot easier than constructing one.

The ability to automatically generate and maintain maps of information on the World Wide Web (WWW) represents a major priority for learners.

These studies examined students' knowledge restructuring and progress toward more meaningful understanding.

Garvie (1994) explored the use of semantic networks in the geosciences, to support learning about the classification of minerals.

In sum, when students engage in the activity of mapping knowledge, they generally tend to learn more and reflect more upon their own learning than with other study methods.

As rate of change increases, thinking and doing both involve increasing complexity traceable to the interdependencies inherent in the systems of interacting parts of an enterprise.

Such models are potentially adaptable because concrete metaphors provide critical support for abstract thinking (Lakoff, 1987; Lakoff & Johnson, 1981).

Concept maps have an abstract structure as typed hypergraphs, and computer support for concept mapping can associate visual attributes with node types to provide an attractive and consistent appearance.

Computer support can also provide interactive interfaces allowing arbitrary actions to be associated with nodes such as hypermedia links to other maps and documents.

Abstract Topic maps are a new ISO standard for describing knowledge structures and associating them with information resources.

As such they constitute an enabling technology for knowledge management.

While it is possible to represent immensely complex structures using topic maps, the basic concepts of the model -- Topics, Associations, and Occurrences (TAO) -- are easily grasped.

Similarly, if you are looking for a particular piece of information in a book (as opposed to enjoying the experience of reading it from cover to cover), a good index is an immense asset.

Different topic types might also be distinguished through the use of explanatory labels following the names, e.g. "Tosca (opera)" and "Tosca (character)".

The concepts of "topic", "topic type", "name", "occurrence" and "occurrence role" allow us to organize our information resources according to topic, and to create simple indexes, but not much more.

The reason for having a special construct for this kind of association is the same as the reason for having special constructs for certain kinds of names (indeed, for having a special construct for names at all): The semantics are so general and universal that it is useful to standardize them in order to maximize interoperability between systems that support topic maps.

Public subjects are a necessary precondition for the widespread use of portable topic maps, since there is no point in offering a topic map to others if it is not guaranteed to "match up" with relevant occurrences in the receiver's pool of information resources.

Facets Sometimes it is convenient to be able to assign metadata to the information resources that constitute the occurrences of a topicfrom within the topic map.

Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments.

Kluwer book series on Computer Supported Collaborative Learning, Pierre Dillenbourg (Series Editor).

Deictic Roles of External Representations in Face-to-face and Online Collaboration.

Designing for Change in Networked Learning Environments, Proceedings of the International Conference on Computer Support for Collaborative Learning 2003, B. Wasson, S. Ludvigsen & U. Hoppe (Eds), Dordrecht: Kluwer Academic Publishers, pp. 173-182..

An Empirical Study of the Effects of Representational Guidance on Collaborative Learning.

Comparing the Roles of Representations in Face to Face and Online Collaborations, Proceedings of the International Conference on Computers in Education, December 3- 6, Auckland.

Learning Object Meta-data for a Database of Primary and Secondary School Resources.

Mapping to know: The effects of evidence maps and reflective assessment on scientific inquiry skills.

Coaching Collaboration by Comparing Solutions and Tracking Participation.

Collaborative Representations: Supporting Face to Face and Online Knowledge-building Discourse.

Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2001), August 6- 8, Madison, Wisconsin, pp. 371-374.

Representational and Advisory Guidance for Students Learning Scientific Inquiry.

That is, they apply a solution procedure.

Although these processes are clearly interrelated, it is possible to study their separate contributions to the success of problem solving and reasoning.

It is critical to study solvers' knowledge of spatial diagram representations to fully understand their use of such representations to support analytical reasoning and problem solving.

See related topics and documents

http://www.serg.sdsu.edu/CRMSE/kfisher_knowrep.html

Untitled

Enhancing cognitive skills for meaningful understanding of domain specific knowledge.

maps representations constructing ability mapping students learning patterns "semantic networks"

Cartography provides a concise means for representing large volumes of data in ways that are visually interesting and comprehensible, and that reveal important patterns and trends.

Graphic visual-spatial representations tap into the power of human pattern recognition.

We argue here that successful visual representation of large bodies of information is the key to success in the information and communication age.

Knowledge mapping has been utterly neglected, in contrast to the great strides made in other forms of cartography.

Most scientists and science education researchers are not enamored with the primitive forms of knowledge mapping currently available, and they have yet to recognize the need for powerful knowledge-mapping capabilities in the future.

Such knowledge transformations necessarily should have options for layers of detail and levels of complexity.

We predict that, in the future, computers will create maps of existing text without human guidance-- -that is, will automatically generate knowledge- distillation products.

There is considerable research regarding knowledge mapping as a constructivist learning activity in which the external map provides a tangible arena for the manipulation and organization of ideas.

Gordon Pask of Great Britain seems like the obvious one to begin this recent history.

He developed maps to represent the ideas that emerged in student conversations and to show the connections between those ideas (Pask, 1975, 1977).

In the same decade but on a different continent, science educator Joseph Novak and his graduate students invented concept mapping as a learning tool for K--12 students (Stewart, Van Kirk, & Rowell, 1979).

Novakian concept maps grew out of Ausubelian learning theory (1963, 1968) with its emphasis on building connections between ideas.

With the advent of the Macintosh personal computer in the early 1980s, Fisher, Faletti, and their colleagues created the SemNet® knowledge mapping software as a learning tool for college biology students (Fisher, Faletti, Patterson, Thornton, Lipson, & Spring, 1990).

The design of this software grew directly out of cognitive science, especially Quillian's semantic network theory (1967, 1968, 1969) for how we store information in long term memory.

Of these various mapping forms, Novakian concept maps and SemNet have enjoyed the greatest benefits of ongoing research and iterative development.

The most powerful knowledge-mapping program currently available to educators is the SemNet® semantic networking software (Fisher et al., 1990) [see Figure 3].

Dr. Robert Abrams at the University of California---Santa Cruz, developer of the LifeMap software, has been working with Fisher to develop a prototype for concept map-Gowin's Vee- SemNet conversions.

Evidence that Using a Knowledge Map (like a road map) Can Promote Learning Good knowledge maps constructed by an expert are effective learning aids.

Animal range maps help students and researchers understand the geographic extent of wildlife species.

The Human Genome Mapping Project illustrates the enormous value placed by scientists on the mapping of the entire human genome.

This is an excellent example of a domain sector that doesn't need to be stored in human memory but is extremely valuable on a look-up-as- needed basis.

Reading a well-designed map is obviously a lot easier than constructing one.

Thus, once this hypothetical tool is invented, perhaps an effective way to use it as a learning tool is to have students use a map to go into the science domain "woods" and then map their own way out, a "Hansel and Gretel" challenge.

Designing Sophisticated Knowledge Mapping Software We need to develop the capability for constructing powerful knowledge maps before we can actually test them.

The ability to automatically generate and maintain maps of information on the World Wide Web (WWW) represents a major priority for learners.

Many search engines use the same "spiders" to compile their indices, so the difference lies in the way they interpret the data and how they allow you to manipulate the results.

These studies examined students' knowledge restructuring and progress toward more meaningful understanding.

Similar positive results are seen in studies in which students engage in semantic networking as a means of mathematics and science learning.

Several studies ranging from 7th grade to college found that biology students using SemNet learned more declarative knowledge and learned the topics more deeply than comparison groups (Gorodetsky & Fisher, 1996; Jay, Alldredge, & Peters, 1990; Christianson & Fisher, 1999).

Garvie (1994) explored the use of semantic networks in the geosciences, to support learning about the classification of minerals.

In sum, when students engage in the activity of mapping knowledge, they generally tend to learn more and reflect more upon their own learning than with other study methods.

Knowledge mapping is the representation of detailed, interconnected, non-linear thought (Fisher & Kibby, 1996).

A SemNet frame or concept map has the advantage of providing not only a coherent graphic representation about a concept or group of concepts, but it also provides a manageable chunk of information which is easily assimilated (see Figure 4).

Simon (1974) estimated that a class A player has a repertoire of about 1,000 such schemata, while a master chess player has between 25,000 and 100,000 chess board schemata stored in memory.

In sum, each domain is characterized by its own unique relations, meaning is captured in the relations, relations are more difficult to master than concepts, and understanding is facilitated by explicitly identifying each relation among important ideas.

Baars (1988) sees the conscious mind as the tip of the iceberg, resting on and supported by a vast array of subconscious modules which work more or less independently from one another and in parallel.

This is one of the more potent "effects of" benefits described by Perkins (1993) that result from constructing a semantic network.

When they are constructing maps of their knowledge, learners typically engage in sustained and high-level thinking and conversation about the topic they are learning.

As noted previously, concept modeling makes concepts explicit (notably including relationships), enabling individuals to comprehend and think about a topic far more powerfully and quickly.

Equally significantly, it enables groups of participants to build and communicate a shared understanding of a domain and to efficiently collaborate on developing it further.

The Fifth Discipline (Senge, 1994) takes up the issues of modeling systems in the context of the business world.

As rate of change increases, thinking and doing both involve increasing complexity traceable to the interdependencies inherent in the systems of interacting parts of an enterprise.

The understanding of concepts and ideas by the use of diagrams and imagery, known as diagrammatic reasoning, differs in significant ways from understanding via linguistic or algebraic representations.

Concept modeling can refer to a number of formal or informal techniques for capturing and manipulating concepts, and for using a vocabulary that provides more structure than simple narrative text.

Of these, the developing UML initiative is particularly interesting, as its real- world-modeling capability expands in an attempt to model everything of interest to business and systems developers---which is an ever- increasing portion of the known universe.

In essence the problem may be stated as a shortcoming in the ability for the individual to acquire or develop sufficiently sophisticated models of a situation sufficiently rapidly.

· Facilitates sharing models publicly--- so that individuals can acquire models already assembled (and possibly contribute refinements).

These include the ability to generate high level overviews of large bodies of information in ways that allow discrimination of important patterns, scalability, differentiation of concepts and the links among them based upon the properties exhibited by these elements within the map, and the ability to transform knowledge rapidly and easily from one representation to another.

Such models are potentially adaptable because concrete metaphors provide critical support for abstract thinking (Lakoff, 1987; Lakoff & Johnson, 1981).

Even as we speak, the crew of the shuttle Endeavor is endeavoring to produce the most accurate map of the world ever made!

According to McNeill and Freiberger (1993), the Japanese were 5--7 years ahead of the U.S. in using this important design tool at the time their book was published.

Concept mapping and Hyper Media

Concept maps have an abstract structure as typed hypergraphs, and computer support for concept mapping can associate visual attributes with node types to provide an attractive and consistent appearance.

Computer support can also provide interactive interfaces allowing arbitrary actions to be associated with nodes such as hypermedia links to other maps and documents.

This article describes a general concept mapping system that is open architecture for integration with other systems, scriptable to support arbitrary interactions and computations, and cutomizable to emulate many styles of map.

It is proposed that concept maps be regarded as basic components of any hypermedia system, complementing text and images with formal and semi- formal active diagrams.

Hypertext systems commenced with simple facilities for creating nonlinear texts by embedding within a text links to other texts (McKnight, Dillon and Richardson, 1991).

Concept maps are a form of diagram specifically targeted to provide visual languages similar in their characteristics to natural language text in that they can be subject to syntactic and semantic constraints, and their representational capacity can range from the fairly informal to the extremely formal.

Concept maps have been used in education, policy studies and the philosophy of science to provide a visual representation of knowledge structures and argument forms.

In many disciplines various forms of concept map are already used as formal knowledge representation systems, for example: semantic networks in artificial intelligence, bond graphs in mechanical and electrical engineering, CPM and PERT charts in operations research, Petri nets in communications, and category graphs in mathematics.

These requirements have been addressed by the development of a general visual language technology supporting customizable interactive concept maps (Gaines and Shaw, 1993c) and semantic networks (Gaines, 1991).

Concept maps may be used for the indexing and retrieval of hypermedia material, providing an attractive, meaningful and easy to use interface.

The following sections give an overview of concept maps and their applications, formal semantics for concept maps, an abstract model of general concept maps, the implementation of a general concept mapping component for hypermedia systems, its user interface and computational capabilities, its application to hypermedia indexing, collaborative applications of linked maps, and integration of concepts map with World-Wide Web systems for wide-area collaboration.

See related topics and documents

http://ksi.cpsc.ucalgary.ca/articles/ConceptMaps/

The Tao of Mapping

"topic maps" indexes standard opera tosca occurrences references index management

Steve Pepper Senior Information Architect Infostream Norway Oslo Infostream, STEP Infotek, Gjerdrums vei 12 Oslo 0486 Norway Phone: +47 22021680 Fax: +47 22021681 email: pepper@infotek.no web site: www.infotek.no Biography Steve Pepper --- Steve Pepper is the Senior Information Architect at STEP Infotek, a division of Infostream specializing in standards-based information reengineering.

He represents Norway on JTC 1/SC 34, the ISO committee responsible for the development of SGML and related standards, and is convenor of WG 3 (Information association), whose responsibilities include the HyTime and Topic Map standards.

Abstract Topic maps are a new ISO standard for describing knowledge structures and associating them with information resources.

As such they constitute an enabling technology for knowledge management.

While it is possible to represent immensely complex structures using topic maps, the basic concepts of the model -- Topics, Associations, and Occurrences (TAO) -- are easily grasped.

This paper provides a non-technical introduction to these and other concepts (the IFS and BUTS of topic maps), relating them to things that are familiar to all of us from the realms of publishing and information management, and attempting to convey some idea of the uses to which topic maps will be put in the future.

Similarly, if you are looking for a particular piece of information in a book (as opposed to enjoying the experience of reading it from cover to cover), a good index is an immense asset.

The answer in the realm of publishing and information management is the new international standard, Topic Maps.

Up until now there has been no equivalent of the traditional back-of- book index in the world of electronic information.

We'll start with indexes, and go on to consider glossaries and thesauri.

A traditional index is in fact a map of theknowledge contained in a book; it lists the topics covered, by whatever name users might be expected to want to look them up, and includes salient (andonly salient) references to those topics.

Different topic types might also be distinguished through the use of explanatory labels following the names, e.g. "Tosca (opera)" and "Tosca (character)".

Topics, Associations and Occurrences are also the key constructs in the topic map model (hence the title of this paper).

But before discussing that model in more detail, let us look briefly at some related navigational aids (glossaries and thesauri), and at one common method of knowledge representation in the domain of artificial intelligence (semantic networks), since those will broaden our understanding of the kind of structures we are dealing with.

This is important because it makes it possible not only to say that two terms are related, but alsohow orwhy they are related.

It also makes it possible to group together terms that are associated in the same way, thus making navigation much easier.

What eventually became the topic map standard almost ten years later was thus based from the start on the basic concepts embodied in indexes, that is Topics, Associations, and Occurrences -- the TAO of topic maps.

Whenever there is a need to distinguish between the two, we use the terms "topic link" and "subject".

So, in the context of adictionary of opera, a topic might represent subjects such as "Tosca", "Madame Butterfly", "Rome", "Italy", the composer "Giacomo Puccini", or his birthplace, "Lucca": that is, anything that might have an entry in the dictionary -- but also much else besides.

Topic types Topics can be categorized according to their kind.

This corresponds to the categorization inherent in the use of multiple indexes in a book (index of names, index of works, index of places, etc.), and to the use of typographic and other conventions to distinguish different types of topics.

Thus, Puccini would be a topic of type "composer", Tosca and Madame Butterfly topics of type "opera", Rome and Lucca topics of type "city", Italy a topic of type "country", etc.

Exactly what one chooses to regard as topics in any particular application will vary according to the needs of the application, the nature of the information, and the uses to which the topic map will be put: In athesaurus, topics would represent terms, meanings, and domains; insoftware documentation they might be functions, variables, objects, and methods; inlegal publishing, laws, cases, courts, concepts, and commentators; intechnical documentation, components, suppliers, procedures, error conditions, etc.

It should be clear that the preceding paragraphs would have been rather more difficult to understand if we hadn't given names to our topics and topic types!

However, topics don'talways have names: A simple cross reference, such as "see page 97", is considered to be a link to a topic that has no (explicit) name.

Instead, it recognizes the need for some forms of name (that have particularly important and universally understood semantics) to be defined in a standardized way, in order for applications to be able to do something meaningful with them, and at the same time the need for complete freedom and extensibility to be able to define application-specific name types.

The concepts of "topic", "topic type", "name", "occurrence" and "occurrence role" allow us to organize our information resources according to topic, and to create simple indexes, but not much more.

Associations A topic association is (formally) a link element that asserts a relationship between two or more topics.

The reason for having a special construct for this kind of association is the same as the reason for having special constructs for certain kinds of names (indeed, for having a special construct for names at all): The semantics are so general and universal that it is useful to standardize them in order to maximize interoperability between systems that support topic maps.

It is not enough to know that Puccini and Verdi participate in an "influenced- by" association; we need to know who was influenced by whom, i.e. who played the role of "influencer" and who played the role of "influencee".

For example, if reference works publishers from Norway, France and Germany were to merge their topic maps, there would be a need to be able to assert that the topics "Italia", "l'Italie" and "Italien" all refer to the same subject.

Public subjects are a necessary precondition for the widespread use of portable topic maps, since there is no point in offering a topic map to others if it is not guaranteed to "match up" with relevant occurrences in the receiver's pool of information resources.

Activities are therefore underway, under the aegis of ISO, OASIS and the GCA, to develop directories of public subjects.

Facets Sometimes it is convenient to be able to assign metadata to the information resources that constitute the occurrences of a topicfrom within the topic map.

Facets are typically used for supplying the kind of metadata that might otherwise have been provided by SGML or XML attributes, or by a document management system.

Facets are generally speakingnot used to qualify the objects in the "topic domain" part of the topic map (i.e. the topics, topic names and associations).

In a sense, facets are orthogonal to the topic map model itself (except to the extent that both facet types and facet value types, like most other things in the topic map standard, are regarded as topics).

As with occurrence role types, it generally makes sense to specify the type of the facet value, since then the power of topic maps can be used to convey more information about it.

Scope The topic map model allows three things to be said about any particular topic: What names it has, what associations it partakes in, and what its occurrences are.

Assignments of topic characteristics are always made within a specific context, which may or may not be explicit.

For example, if I (yet again) mention "tosca", I should expect my readers to think of the opera by Puccini (or its principle character), because of the context that has been set by the examples used so far in this paper.

For an audience of bakers, however, the name "tosca" has quite other and sweeter connotations: it denotes another topic altogether.

In fact, the well-designed, consistent and imaginative use of scope in topic maps does much more than simply remove ambiguity.

It can also aid navigation, for example by dynamically altering the view on a topic map based on the user profile and the way in which the map is used.

For example, any user that declares a specific interest in opera (or a specific lack of interest in baking!) can have the various toscas ranked accordingly.

Conclusion Topic maps started life as a way of representing the knowledge structures inherent in traditional back of book indexes, in order to solve the information management problems involved in creating, maintaining and processing indexes for complex documentation.

See related topics and documents

http://www.infoloom.com/gcaconfs/WEB/paris2000/S11-01.HTM#N27

Laboratory Interactive Learning

Suthers "PDF preprint" learning conference Collaboration education "International Conference" Representations "Artificial Intelligence" science "Computer Support" "tutoring systems" "Representational Guidance" inquiry online

Reverse chronological order by year, alphabetically by authors and titles within each year.

Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments.

Kluwer book series on Computer Supported Collaborative Learning, Pierre Dillenbourg (Series Editor).

BRIX --- Elements for Language Course Creation.

Proceedings of ED-MEDIA 2003: World Conference on Educational Multimedia, Hypermedia & Telecommunications (Honolulu, June 23-28, 2003), D. Lassner & C. McNaught (Eds.), Norfolk: Association for Advancement of Computing in Education, pp. 415- 422.

The Influence of Knowledge Modeling on the Communication Process.

(KI is edited by the Gesellschaft für Informatik e.V. and is printed by arendtap, Bremen, Germany, ISSN 0933-1875.)

(Keynote address for 11th International Conference on Artificial Intelligence in Education: AI-ED 2003.)

Deictic Roles of External Representations in Face-to-face and Online Collaboration.

Designing for Change in Networked Learning Environments, Proceedings of the International Conference on Computer Support for Collaborative Learning 2003, B. Wasson, S. Ludvigsen & U. Hoppe (Eds), Dordrecht: Kluwer Academic Publishers, pp. 173-182..

An Empirical Study of the Effects of Representational Guidance on Collaborative Learning.

Coaching Collaboration in a Computer- Mediated Learning Environment.

In Proceedings of Computer Support for Collaborative Learning 2002, Hillsdale: Lawrence Erlbaum Associates, January 7-11, Boulder, pp. 583-584.

Coaching Web-based Collaborative Learning based on Problem Solution Differences and Participation.

International Journal of Artificial Intelligence in Education 13, Online: http://www.cogs.susx.ac.uk/ijaied/abstracts/Vol_13/constantino.html.

(To appear in print version as well.)

The Roles of Representations in Online Collaborations, paper presented at the Annual Meeting of the American Educational Research Association (AERA), New Orleans, April 1-5, 2002.

The Effects of Representation on Students' Elaborations in Collaborative Inquiry, In Proceedings of Computer Support for Collaborative Learning 2002, Hillsdale: Lawrence Erlbaum Associates, January 7-11, Boulder, pp. 472-480.

Comparing the Roles of Representations in Face to Face and Online Collaborations, Proceedings of the International Conference on Computers in Education, December 3- 6, Auckland.

Learning Object Meta-data for a Database of Primary and Secondary School Resources.

Kukakuka: An Online Environment for Artifact-Centered Discourse, Education Track of the Eleventh World Wide Web Conference (WWW 2002), Honolulu, May 7-11, 2002, pp.472-480.

Online Workspaces for Annotation and Discussion of Documents.

Proceedings of the International Conference on Computers in Education, December 3-6, Auckland, New Zealand.

Mapping to know: The effects of evidence maps and reflective assessment on scientific inquiry skills.

Coaching Collaboration by Comparing Solutions and Tracking Participation.

In P. Dillenbourg, A. Eurelings, K. Hakkarainen (Eds.) European Perspectives on Computer- Supported Collaborative Learning, Proceedings of the First European Conference on Computer- Supported Collaborative Learning, Universiteit Maastricht, Maastrict, the Netherlands, March 22- 24 2001, pp. 173-180.

Designing and Evaluating a Collaboration Coach: Knowledge and Reasoning.

In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.) Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future (10th International Conference on Artificial Intelligence in Education), Amsterdam: IOS press, May 19-23, San Antonio Texas, pp. 176-187.

Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2001), August 6- 8, Madison, Wisconsin, pp. 25-28.

Collaborative Representations: Supporting Face to Face and Online Knowledge-building Discourse.

Proceedings of the 34th Hawai`i International Conference on the System Sciences (HICSS-34), January 3-6, 2001, Maui, Hawai`i (CD-ROM), Institute of Electrical and Electronics Engineers, Inc. (IEEE).

Evaluating the Learning Object Metadata for K-12 Educational Resources.

Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2001), August 6- 8, Madison, Wisconsin, pp. 371-374.

Towards a Systematic Study of Representational Guidance for Collaborative Learning Discourse.

Representational and Advisory Guidance for Students Learning Scientific Inquiry.

Smart machines in education: The coming revolution in educational technology.

Learning by Constructing Collaborative Representations: An Empirical Comparison of Three Alternatives.

In P. Dillenbourg, A. Eurelings, K. Hakkarainen (Eds.) European Perspectives on Computer- Supported Collaborative Learning, Proceedings of the First European Conference on Computer- Supported Collaborative Learning, Universiteit Maastricht, Maastrict, the Netherlands, March 22- 24 2001, pp. 577-584.

Helping students articulate, support, and criticize scientific explanations.

Report on the AAAI-91 workshop on comparative analysis of explanation planning architectures.

See related topics and documents

http://lilt.ics.hawaii.edu/lilt/research/pubs.html

Laura Novack

It is important to distinguish between representations and solution procedures.

Solvers typically begin their problem- solving efforts by trying to understand, at least at a rudimentary level, the underlying structure of the problem.

That is, they apply a solution procedure.

Although these processes are clearly interrelated, it is possible to study their separate contributions to the success of problem solving and reasoning.

I am interested in students' knowledge and use of three, external, spatial diagram representations -- matrices, networks (i.e., path diagrams), and hierarchies -- that are important tools for thinking both in everyday situations and in formal domains (Novick, 2001).

Similarly, a network can be used to represent the flight paths for an airline, the friendships between people at a conference, or the (hypothesized) structure of semantic memory.

It is critical to study solvers' knowledge of spatial diagram representations to fully understand their use of such representations to support analytical reasoning and problem solving.

The results of several experiments (Novick, Hurley, & Francis, 1999) suggest that college students have at least rudimentary abstract, rule-based knowledge concerning the applicability conditions for the three spatial diagrams.

The results of a more recent study in which subjects had to choose the most appropriate type of spatial diagram for scenarios written in a specific content domain versus completely abstractly provide more direct evidence that students' representation selections are based, at least in part, on abstract, rule-based knowledge (Hurley & Novick, 2002).

The past decade or so has seen increased attention in the mathematics education community to the goal of developing numeracy -- the mathematical counterpart to literacy -- among school children.

The National Council of Teachers of Mathematics, in their recently- published (2000) standards, emphasizes that K-12 students need to obtain more sophisticated, and explicit, knowledge of culturally- significant types of abstract, mathematical diagrams -- including the three types of diagrams I have been investigating in my research.

In a recent manuscript (Novick, in press), I proposed a model of diagram literacy that specifies six types of knowledge that students should possess to demonstrate diagrammatic competence -- implicit, construction, similarity, structural, metacognitive, and translational.

The results of this study also provided important preliminary information concerning how students' knowledge of the structural properties is organized in memory and how the organization varies as a function of expertise (Novick, 2001; Novick & Hurley, 2001).

The second question concerns the relative importance or diagnosticity of the applicability conditions; presumably, not all applicability conditions are equally diagnostic cues for the use of a particular type of diagram.

For example, the fact that the items in a particular situation are organized into levels is probably a more diagnostic cue for a hierarchy representation than is the fact that there are directional links between the items.

For his dissertation, my graduate student, Sean Hurley, recently completed three studies concerning diagram construction and use.