Using Deep Knowledge Tracing to Predict a Simulated Distance Measure

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dc.contributor.advisorLeone, F.
dc.contributor.authorOckers, F.
dc.date.issued2018-07-12
dc.description.abstractKnowledge 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.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/7043
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleUsing Deep Knowledge Tracing to Predict a Simulated Distance Measureen_US
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