Using Deep Knowledge Tracing to Predict a Simulated Distance Measure
dc.contributor.advisor | Leone, F. | |
dc.contributor.author | Ockers, F. | |
dc.date.issued | 2018-07-12 | |
dc.description.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. | en_US |
dc.embargo.lift | 10000-01-01 | |
dc.embargo.type | Permanent embargo | en_US |
dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/7043 | |
dc.language.iso | en | en_US |
dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
dc.thesis.specialisation | Bachelor Artificial Intelligence | en_US |
dc.thesis.studyprogramme | Artificial Intelligence | en_US |
dc.thesis.type | Bachelor | en_US |
dc.title | Using Deep Knowledge Tracing to Predict a Simulated Distance Measure | en_US |
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