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Acquiring Structural Knowledge

D. H. Jonassen and Sh. Wang (1993)

Reviewed by Chris


Contents
Abstract

Literature

The experiments of Jonassen and Wang

Hot links


Abstract
Jonassen and Wang review the results of three studies that assess the effects of different methods for explicitly mapping expert knowledge structure onto hypertext on the acquisition of structural knowledge in the learners.
What really helps learners to improve structural knowledge significantly: working on a task of generating a semantic network.

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Literature
Jonassen and Wang 1993
David H. Jonassen and Sherwood Wang: Acquiring Structural Knowledge from Semantically Structured Hypertext.
In: Journal of Computer-Based Instruction, Winter 1993, Vol. 20, No. 1, 1-8.

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Jonassen and Wang: Structural knowledge acquisition
Associative hypertext structures

Hypertext information is knowledge-based with organized relations between the content units.
Can hypertext mimic human associative networks?

The schema theory of Rumelhart and Ortony (1977) claims that personal knowledge is stored in information packets or schemas that comprise our mental constructs for ideas. Each schema we construct represents a mini-framework in which to interrelate elements or attributes of information about a topic into a single conceptual unit.
These mini-frameworks are organized by the individual into a larger network of interrelated constructs known as a semantic network. These networks are composed of nodes: representations of schemas. Ordered labelled relationships define the propositional relationship between the nodes.

Semantic networks are somewhat transferable: as a result of instruction, learners' knowledge structures more closely resemble the instructor's knowledge structure.
So, learners are acquiring two things during instruction:
- knowledge and
- knowledge structures that mimic the teacher's knowledge structure.

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Hypotheses Acquiring knowledge structures

Mapping the semantic network of an expert or knowledgeable person onto the structure of a hypertext will contribute to the development of the learner's knowledge structures while using the hypertext to learn.

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Experiment 1 Three different ways learning from hypertext

  • The semantic network of an expert is shown in form of a graphical map: a spatial description of the nodes and the relationships between them.
  • This picture is used as a graphical browser between the users and the hypertext.
  • Users navigating the web of hypertext nodes and links do less get lost since the graphical browser provides them an informative overview.
  • Users navigating through the hypertext is actually navigating through the expert's knowledge structure.

    Investigation

    To what extent will users model or replicate the knowledge structure of the expert shown in the graphical browser in their own knowledge representations?

    Method

  • Special instruments assessed the learners' structural knowledge acquisition, i.e., the knowledge of how concepts within a domain are interrelated, which mediates the translation of declarative knowledge into procedural knowledge.
  • Measured aspects of structural knowledge:
    - relationship proximity judgements: How strong is a relationship between two concepts?
    - semantic relationships subscale: Nature of the relationship between two concepts?
    - analogies subscale: Which term suits best?

  • Another set of instruments assessed the learners' recall of the information in the hypertext.

  • Materials: the HyperCard version of the book Jonassen (1989): Hypertext / Hypermedia:
    - three HyperCard stacks
    - 75 major concepts nodes
    - 1167 links
    - 240 screens

  • Three groups:
    - Experimental group 'map': graphical browser providing explicit structural information about the nodes and links in the hypertext.
    - Test group 'pop-up': pop-up window providing explicit structural information about the nodes and links in the hypertext.
    - Control group: no structural information, just an unstructured list of nodes.

  • Students were assigned the task of learning about the new instructional technology hypertext. A monitoring program recorded each action and time allotted to each of the student's actions.
  • Following the individual interaction, learners completed the posttests.

    Results and discussion

    The dependent variables under analysis were:
    - recall (15 items)
    - relationship subscale (10 items)
    - proximity subscale
    - analogy subscale (10 items)

    The independent variable was:
    - treatment version (graphical browser vs. list)

  • The recall variable was the only dependent variable even approaching significance: the control group (list) outperformed the test group (map). Map group = lowest recall!
    Interacting time with hypertext (less than an hour) too short for the complex structural informations?
  • Pop-up group worked fastest, control group spent the longest amount of time.
  • The number of structural cards accessed was significantly different: most map, least control. The total time spent viewing structural cards did not differ significantly.
  • The total number of cards accessed did not differ significantly.
  • The number of high level structural cards (main map and 8 first level maps) accessed significantly predicted the relationship scores.
  • The number structural cards accessed significantly predicted the time spent.
  • Structural information in the hypertext did affect the amount of structural knowledge acquired: The number of structural cards accessed and time spent with the structural cards predicted structural knowledge acquisition.
  • Learners were not familier with higher order and structural knowledge tasks:
    - relationship test: only half of the questions answered correctly;
    - analogy test: less than one third of the questions answered correctly;
    - paraphrased recall test: half of the questions answered correctly.

  • Limited potential of hypertext for learning:
    Are learners able to acquire knowledge from an information retrieval hypertext?

  • Generativity in information processing:
    - Meaningful learning results from generative processing in which learners relate new information to their existing knowledge in order to accomodate it with what they know already.
    - Merely attending to structural cues may not engender generative processing of information.

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  • Experiment 2 Effects of generative processing activity

  • Learning requires that users not only access information but also interpret it by relating it to prior knowledge.

    Investigation

    Do learners perform better when asked to generate their own interpretation of the links?

    Method

  • The same instruments (as in experiment 1):
    - for measuring the learners' structural knowledge acquisition,
    - the hypertext to be treated,
    - the experimental procedures.

  • Again three groups:
    - New experimental group 'generative': learners determine the nature of the link relationships for themselves, rather than being informed by the program. A pop-up window presented 12 different link types and required learners to classify the link type that most accurately described the nature of the relationship implied by the link.
    - Test group 'pop-up', like in experiment 1: again pop-up window providing explicit structural information about the nodes and links in the hypertext
    - Control group, like in experiment 1: again no structural information, just an unstructured list of nodes.

    Results and discussion

  • Control group best recall scores.
    Were they less distracted by structural knowledge activities?

  • Field independent subjects (they prefer to structure information rather than to have it structured for them)
    - performed better on the recall task when learning in the control or generative set-up;
    - were impeded by the pop-up version;
    - performed better on the semantic relationship task when learning in the pop-up version.

  • Recall from unstructured hypertext was predictably higher than from explicitly structured hypertext.
  • The generative treatment did not appear to engage the learners in generative processing.
  • Learners were not engaged in meaningful learning by the assigned learning task.

  • Since learners were unfamiliar with structural knowledge outcomes, they had no reason to attend to the structural cues or activities.

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  • Experiment 3 Semantic networking as an integrative strategy

  • Probably the most important determinant of learning is:
    - the awareness,
    - acceptance, and
    - understanding ot the required task or learning outcome.
  • Students are most familiar with recall-oriented learning tasks. Hence, they are not certain how to learn from hypertext.
  • Students are not familiar or competent with structural knowledge tasks.

  • A significant problem in learning from hypertext is
    - the integration of what is acquired into the learner's knowledge structure and
    - the restructuring of those knowledge structures.
  • Semantic networking software may be used as a cognitive learning strategy to help learners integrate information from hypertext.

    Investigation

    Do integration and restructuring activities ensure learning from hypertext?

    Method

  • The same instruments (like in experiments 1 and 2)
    - for measuring the learners' structural knowledge acquisition,
    - the hypertext to be treated,
    - the experimental procedures.

  • Semantic networking software: Learning Tool (Kozma, 1987)

  • Effects of first treatment factor: structural knowledge acquisition resulting from semantic networking:
    For this purpose, now two groups:
    - New experimental group 'semantic net': learners were required to construct a semantic network about the topic of the hypertext following the study period. Previously, they had gained proficiency with 'Learning Tool'.
    - Control group 'read only': learners were told only to study the hypertext to acquire knowledge during the study period.

  • Effects of second treatment factor: level of structural support provided:
    - the control treatment (list) providing no structural cues in the browser and
    - the graphical browser treatment providing explicit information.

    Results and discussion

  • Learners who were given the task of creating a semantic network performed significantly better on the relationship judgements.
    Structural knowledge acquisiton improved significantly by focusing the learner's attention on structural aspects of the information in the hypertext.

  • Best performance on analogy subscale had learners with
    - visual support of a graphical browser and
    - focusing on structural relationships.

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  • General discussion Attention to structural information

  • Merely showing learners structural relationships is probably not sufficient to result in structural knowledge encoding.
  • Getting learners to focus on structural relationships enhances structural knowledge acquisition, which supports higher order thinking in the form of analogical reasoning.

    Cognitive limitation of browsing behavior

  • What matters most in learning is the construction of personally relevant knowledge structures.
  • Learning from hypertext must rely on externally imposed or mediated learning tasks.
  • Hypertext may not be very appropriate for highly structured learning tasks.

    Hypertext literacy

  • More effective use of hypertexts by learning first hypertext processing strategies.
  • Hypertext-literate learners have developed a useful set of strategies for navigating and integrating information from hypertext.

    Structural knowledge acquisition

  • Learners who used structural knowledge learning strategies increased their structural knowledge acquisition.
  • The ability to engage in meaningful learning helps more than ability to retrieve information.

    Individual differences and learning from hypertext

  • Field independent processors
    - prefered to impose their own structure on information,
    - were impeded by the structural information provided, - are better hypertext processors - especially as the form of the hypertext becomes more referential and less overtly structured.

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  • Links
    David H. Jonassen
  • David Jonassen, Professor in Charge of Instructional Systems
  • Jonassen, David H.; Wang, Sherwood (1994): The Physics Tutor: Integrating Hypertext and Expert Systems (abstract ERIC)
  • Physics Tutor, Contents in Current Page: Physics Tutor - Introduction
  • Koneman, Philip A.; Jonassen, David H. (1994): Hypertext Interface Design and Structural Knowledge Acquisition (abstract ERIC)
  • Jonassen, David H.; Wang, Sherwood (1993): Acquiring Structural Knowledge from Semantically Structured Hypertext (abstract ERIC)
    - Jonassen (1993), German abstract of Jonassen & Wang (1993) by H. Gerdes
  • Jonassen, David H.; Wang, Sherwood (1992): Collaborative Annotation of a Hyperbook on Hypermedia Design (abstract ERIC)
  • Jonassen, David H.; Wang, Sherwood (1992): Acquiring Structural Knowledge from Semantically Structured Hypertext (abstract ERIC)
  • Jonassen, David H.; Grabinger, R. Scott (1992): Applications of Hypertext: Technologies for Higher Education (abstract ERIC)
  • Jonassen, David H. (1991): Hypertext as Instructional Design (abstract ERIC)
  • Wilson, Brent G.; Jonassen, David H. (1989): Hypertext and Instructional Design: Some Preliminary Guidelines (abstract ERIC)
  • Jonassen, David H. (1988): Designing Structured Hypertext and Structuring Access to Hypertext (abstract ERIC)
  • David Jonassen: A Manifesto for a Constructivist Approachto Technology in Higher Education
  • David H. Jonassen: ITForum Paper #1 Technology as Cognitive Tools: Learners as Designers

    Others

  • Colazzo, Luigi. Molinari, Andrea: Using Hypertext Projection to Increase Teaching Effectiveness (abstract)
  • Martin Ryder: Production and consumption of meaning: The interplay between subject and object in open hypertext representation

    Bibliography

  • Hypertext-Literatur, hypertext bibliography by H. Gerdes

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