Difference between revisions of "CUMULATE parametrized asymptotic knowledge assessment"

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==Example 1==
 
==Example 1==
[[Image:CUMULATE parameterized asymptotic knowledge assessment - knowledge growth and penalty.png|thumb|120px|([http://bit.ly/6r1qz8 alternatively]) via [http://code.google.com/apis/chart/ Google Chart API])]]
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Below is a graph of concept's knowledge level growth and the penalty coefficient vs. number of successful attempts to apply it in a problem. The lines denote:
 
 
To the left is a graph of concept's knowledge level growth and the penalty coefficient vs. number of successful attempts to apply it in a problem. The lines denote:
 
 
* the blue line denotes a successful use of a concept in a new problem (''pV'' = .5)
 
* the blue line denotes a successful use of a concept in a new problem (''pV'' = .5)
 
* the green line denotes a penalty coefficient -- 1/''(<sub>succ</sub>att<sub>p</sub>''+2)''<sup>OPP</sup>'' -- as if it was the same problem (''OPP'' = .25)
 
* the green line denotes a penalty coefficient -- 1/''(<sub>succ</sub>att<sub>p</sub>''+2)''<sup>OPP</sup>'' -- as if it was the same problem (''OPP'' = .25)
 
* the red line ''merges'' the two graphs above
 
* the red line ''merges'' the two graphs above
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[[Image:CUMULATE parameterized asymptotic knowledge assessment - knowledge growth and penalty.png]]
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([http://bit.ly/6r1qz8 alternatively] via [http://code.google.com/apis/chart/ Google Chart API])]]
  
 
==Example 2==
 
==Example 2==

Revision as of 21:49, 20 January 2010

CUMULATE's parameterized asymptotic knowledge assessment algorithm is an attempt to overcome shortcomings of its non-paramterized version. Namely, one-fits-all nature, prohibiting parameter tuning for individual users' abilities and problem complexities.

Computation

The formula below is used to update the knowledge levels of concepts (c) addressed in a problem (p). This formula reflects the following principles (identical to the predecessor algorithm).

  • there are several domain concepts (knowledge items, rules, productions) involved in solving a problem; the knowledge of each of them is updated proportionally to the others
  • knowledge is updated only upon correct user answers, there is no penalty for errors
  • solving a problem correctly multiple times will result in diminishing update (growth) of the knowledge level of the concepts as the number of successes grows

in addition:

  • the initial level of knowledge, speed of knowledge growth, and penalty for repetitive (correct) solutions to the problem - are now adjustable parameters

CUMULATE parameterized asymptotic knowledge assessment.png, where

  • Ko - is the starting level of knowledge, Ko ∈ [0, 1]
  • res - result of user action (0 -error, 1 - correct);
  • Wc,p - is a weight of concept c in problem p
  • ΣWc,p - is the sum of weights of all concepts in problem p
  • succattp - is a number of successful solutions to problem p prior to current attempt
  • pV - speed of knowledge growth parameter, pV ∈ [0, 1]
  • OPP - over-practicing parameter, controlling the penalty for repetitively solving one problem (correctly), OPP ∈ [0, 1]

Examples

Example 1

Below is a graph of concept's knowledge level growth and the penalty coefficient vs. number of successful attempts to apply it in a problem. The lines denote:

  • the blue line denotes a successful use of a concept in a new problem (pV = .5)
  • the green line denotes a penalty coefficient -- 1/(succattp+2)OPP -- as if it was the same problem (OPP = .25)
  • the red line merges the two graphs above

CUMULATE parameterized asymptotic knowledge assessment - knowledge growth and penalty.png
(alternatively via Google Chart API)]]

Example 2

To the left: the speed of problem learning is held constant (pV = .5) while the penalty for practicing the same problem changes.

Example 3

Graph below shows differences the growth of knowledge while speed of learning varies. No penalty is given.

CUMULATE parameterized asymptotic knowledge assessment - knowledge growth for diff pV.png
(alternatively via Google Chart API)

Studies

Contacts

Michael V. Yudelson