dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Sosnovsky, Sergey | |
dc.contributor.advisor | Brinkhuis, M.J.S. | |
dc.contributor.author | Rashid, S. | |
dc.date.accessioned | 2019-06-25T17:01:06Z | |
dc.date.available | 2019-06-25T17:01:06Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/32751 | |
dc.description.abstract | Large volumes of educational data are being collected by online learning environments. This data can be used to track progress of learners and adjust their learning experience in an individually-optimised way. One important challenge in this regard is to effectively recognise typical misconceptions responsible for frequent patterns of erroneous learning behaviour. The purpose of this thesis is to develop a method for identifying such misconceptions in the stream of educational activity. The developed method is evaluated in a data-mining experiment conducted over a large dataset of learners’ responses to arithmetic exercises. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | en | |
dc.title | Misconception elicitation from the logs of an educational system | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Misconception elicitation, arithmetic exercises, multiplication, association rule mining, network analysis, community detection | |
dc.subject.courseuu | Business Informatics | |