Table of Contents: Surmise Systems and Knowledge Spaces
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Surmise Systems and Knowledge Spaces


Surmise systems and Knowledge Spaces:

By dropping the property of -closure Doignon and Falmagne (1985) arrived at the definition of a so-called `surmise system'.

Definition:

Let be a set of problems, and a so-called surmise function which assigns to each element a non-empty family of subsets of , the so-called clauses for . Then, together with is called a surmise system on problems.

A clause of contains a set of prerequisites of minimal with respect to in the sense that if is also a clause for and , then =.


Generalization of Birkhoff's (1937) theorem by Doignon and Falmagne (1985): Every surmise system uniquely determines a set of knowledge states which is closed under union and which contains and .
Such a set of states is called a knowledge space.
Clauses of a surmise system are elements of such a knowledge space. Any state of a knowledge space can be obtained by a union of specific clauses.


Example:
  • Surmise function for the above introduced 4-problem set:
  • Interpretation: Each participant who can solve problem is also able to solve problem or ; each participant who can solve problem is also able to solve problem .
  • Problem sets or are supposed to be `prerequisites' for problem . is the prerequisite for .
  • Surmise systems can be visualized by so-called `AND-OR graphs'.
  • Or-nodes are marked (sometimes) by a curved line and a V symbol.
  • And-nodes don't have a special mark.

    Figure 2: AND-OR graph for the example problem set .

The knowledge space resulting from the surmise system is

- no longer -closed.

Example:

  • Figure 3 shows an example of an and-or-graph of a domain with five items.
  • Knowing a person masters item c, it can be surmised that this person will also master items d and e.
  • Which problem/problems is/are a prerequisite for other items?
  • To master item a it is sufficient to know about item b or item c (or both of them).
  • A person has to understand about both items d and e in order to master item c.
  • In this sense, items d and e are prerequisites for c.

Figure 3: Example of an and-or-graph for 5 items.

 

  • What are the clauses?
  • Interpretation of Q according to Baumunk (1995):

Items:
a Subtraction of uncommon fractions.
b Addition of mixed numbers.
c Addition of uncommon fractions.
d Addition of common fractions.
e Finding the least common multiple.

  • The corresponding knowledge space is the set of all subsets of the domain set which conform to the relationships mentioned before:

  • Subset {b} is not a member of the knowledge space, since item d is a prerequisite for item b, which yields as the smallest (with regard to the ordering given by set inclusion) member of the knowledge space which contains item b.
  • A knowledge space on a set of items has (by definition) the following three properties:

  • means a knowledge space is closed under union.

How can a surmise relation or a surmise function actually be established?

Several approaches that have been developed in the past decade of research include
1. querying experts
2. a posteriori analysis of mass data
3. analysis of didactics and curricula (not worked out yet)
4. systematical problem construction
5. analysis of skills, demands, competences (latent structures)



Mass data analysis

  • Collect answer patterns in a given domain from a large number of subjects.
  • Problem: possible knowledge states may increase exponentially with the number of items considered
  • Resulting knowledge structures will be dependent on the sample, and thus might be no longer valid when applied to other populations.
  • If the data can be collected without too much effort, and the domain does not consist of a large number of items, analyzing mass data might be worth trying.
  • New York: passing the Regents Competency Test in Mathematics is a requirement for graduation from high school.
  • Villano (1991): 20 items from this test and constructed a knowledge structure both by analyzing the answer patterns of 67,204 participants in the test and the judgements of experts.


Querying Experts

  • Query experts on prerequisite relationships
  • First step: the domain of knowledge to be investigated has to be defined; relevant items within the domain have to be identified
  • Second step: relationships between these items have to be determined.
  • Kaluscha (1994): computer based querying procedure based on the algorithm due to Dowling (1993).
  • The experts have to judge standard form assertions, e.g.

    Imagine a person who does not master the items .
    Is it then (practically) certain that this person does not master item ?

    The items are called the premise (set), and the item q is called consequence. The index k is called the size of the premise. For judged assertions, the index k takes values between 1 and where is the total number of items considered.

  • The experts either accepts or rejects these assertions.
  • Inferences can be drawn from previous judgements, and thus the number of standard form assertions to be presented to the experts can be reduced.
  • If a suitable querying strategy is used the necessary number of judgements is again considerably reduced.


References:

Doignon, J.-P. & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge.
International Journal of Man-Machine Studies, 23, 175-196

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