Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

One-digit multiplication errors are one of the most exten- sively analysed mathematical problems. Research work pri- marily emphasises the use of statistics whereas learning an- alytics can go one step further and use machine learning techniques to model simple learning misconceptions. Prob- abilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence learning decision processes.
Original languageEnglish
Pages449-453
DOIs
Publication statusPublished - 26 Apr 2016
EventSixth International Conference on Learning Analytics & Knowledge - Edingburg, United Kingdom
Duration: 25 Apr 201629 Apr 2016

Conference

ConferenceSixth International Conference on Learning Analytics & Knowledge
CountryUnited Kingdom
CityEdingburg
Period25/04/1629/04/16

Fields of Expertise

  • Information, Communication & Computing

Cite this

Taraghi, B., Saranti, A., Legenstein, R., & Ebner, M. (2016). Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. 449-453. Paper presented at Sixth International Conference on Learning Analytics & Knowledge, Edingburg, United Kingdom. https://doi.org/10.1145/2883851.2883895