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Towards a More Accurate
Knowledge Level Estimation
Provenance IEEE 2009 Sixth International Conference on
Information Technology: New Generations.
Author Samad Kardan , Ahmad Kardan
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Outline
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Introduction
The proposed method
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Credit-Blame problem
The skill hierarchies
Guess-slip problem
Implementation and results
Summary
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Introduction
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Computer Adaptive testing (CAT)
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Test items are selected according to the learner’s
level of ability.
Fast、Accuracy.
Need a huge test items pool.
Possibly trying to guess.
Not fairness.
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Computer Based Testing (CBT)
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No need for a large test item pool.
Questions are fixed for all the students.
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The proposed method
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Credit-Blame problem.
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When the user answers a question correctly, this
means that more credit is granted to the skill
nodes with higher level of mastery.
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The skill hierarchies
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A question may require up to three skills.
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Knowledge model for a learning objective.
Skills
Questions
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Guess-slip problem.
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Question model
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A possible guess.
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A possible slip.
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Implementation and results
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The actual and predicted scores.
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Comparison of the actual and predicted
scores.
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Summary
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In this paper, proposed a method for
estimation of the knowledge level of the
students in an e-learning system.
Question model that handles the guess-slip
problem.
knowledge model based on the learning
objects hierarchy.
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