Insights into Learning Competence Through Probabilistic Graphical Models

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over. However, the majority of related work is focusing on descriptive statistics on data from multiple surveys. The goal of our research is to gain insights into multiplication misconceptions by applying machine learning techniques. To reach this goal, we trained a probabilistic graphical model of the students’ misconceptions from data of an application for learning multiplication. The use of this model facilitates the exploration of insights into human learning competence and the personalization of tutoring according to individual learner’s knowledge states. The detection of all relevant causal factors of the erroneous students answers as well as their corresponding relative weight is a valuable insight for teachers. Furthermore, the similarity between different multiplication problems - according to the students behavior - is quantified and used for their grouping into clusters. Overall, the proposed model facilitates real-time learning insights that lead to more informed decisions.
Originalspracheenglisch
TitelMachine Learning and Knowledge Extraction
UntertitelThird IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings
Redakteure/-innenAndreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl
ErscheinungsortCham
Herausgeber (Verlag)Springer International Publishing AG
Seiten250-271
ISBN (elektronisch)978-3-030-29726-8
ISBN (Print)978-3-030-29725-1
DOIs
PublikationsstatusVeröffentlicht - 2 Sep 2019
Veranstaltung2019 International Cross-Domain Conference - Canterbury, Großbritannien / Vereinigtes Königreich
Dauer: 26 Aug 201929 Aug 2019

Publikationsreihe

NameLecture Notes in Computer Science
Band11713

Konferenz

Konferenz2019 International Cross-Domain Conference
KurztitelCD-MAKE 2019
LandGroßbritannien / Vereinigtes Königreich
OrtCanterbury
Zeitraum26/08/1929/08/19

Fields of Expertise

  • Information, Communication & Computing

Dieses zitieren

Saranti, A., Taraghi, B., Ebner, M., & Holzinger, A. (2019). Insights into Learning Competence Through Probabilistic Graphical Models. in A. Holzinger, P. Kieseberg, A. M. Tjoa, & E. Weippl (Hrsg.), Machine Learning and Knowledge Extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings (S. 250-271). (Lecture Notes in Computer Science; Band 11713). Cham: Springer International Publishing AG . https://doi.org/10.1007/978-3-030-29726-8_16