Using Deep Knowledge Tracing to Predict a Simulated Distance Measure
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2018-07-12
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en
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Abstract
Knowledge tracing concerns keeping track of the skills a student has learned over
time by doing exercises that are labelled with a skill. Various ways of knowl-
edge tracing include Bayesian Knowledge Tracing (BKT), Performance Factor
Analysis (PFA) and Deep Knowledge Tracing (DKT). DKT uses a Recurrent
Neural Network (RNN) in combination with Long Short Term Memory units
to predict the performance of students. The binary output of this RNN depicts
the performance of a student. If a student is wrong, then this output will not
give information about how wrong a student was. Therefore, a distance measure
will be added as a second output to the RNN. This distance measure is de ned
as the distance between the real answer and the one given by the student. In
this thesis a DKT model is build with this simulated distance measure as addi-
tional input and output. Unfortunately, the accuracy of these predictions was
insu cient. This is probably due to a wrong interpretation of the nature of this
problem and therefore future research is needed.
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Faculteit der Sociale Wetenschappen