Insights into Learning Competence Through Probabilistic Graphical Models

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction
Subtitle of host publicationThird 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
EditorsAndreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages250-271
ISBN (Electronic)978-3-030-29726-8
ISBN (Print)978-3-030-29725-1
DOIs
Publication statusPublished - 2 Sep 2019
Event2019 International Cross-Domain Conference - Canterbury, United Kingdom
Duration: 26 Aug 201929 Aug 2019

Publication series

NameLecture Notes in Computer Science
Volume11713

Conference

Conference2019 International Cross-Domain Conference
Abbreviated titleCD-MAKE 2019
CountryUnited Kingdom
CityCanterbury
Period26/08/1929/08/19

Fields of Expertise

  • Information, Communication & Computing

Cite this

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 (Eds.), 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 (pp. 250-271). (Lecture Notes in Computer Science; Vol. 11713). Cham: Springer International Publishing AG . https://doi.org/10.1007/978-3-030-29726-8_16

Insights into Learning Competence Through Probabilistic Graphical Models. / Saranti, Anna; Taraghi, Behnam; Ebner, Martin; Holzinger, Andreas.

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. ed. / Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Edgar Weippl. Cham : Springer International Publishing AG , 2019. p. 250-271 (Lecture Notes in Computer Science; Vol. 11713).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Saranti, A, Taraghi, B, Ebner, M & Holzinger, A 2019, Insights into Learning Competence Through Probabilistic Graphical Models. in A Holzinger, P Kieseberg, AM Tjoa & E Weippl (eds), 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. Lecture Notes in Computer Science, vol. 11713, Springer International Publishing AG , Cham, pp. 250-271, 2019 International Cross-Domain Conference, Canterbury, United Kingdom, 26/08/19. https://doi.org/10.1007/978-3-030-29726-8_16
Saranti A, Taraghi B, Ebner M, Holzinger A. Insights into Learning Competence Through Probabilistic Graphical Models. In Holzinger A, Kieseberg P, Tjoa AM, Weippl E, editors, 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. Cham: Springer International Publishing AG . 2019. p. 250-271. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-29726-8_16
Saranti, Anna ; Taraghi, Behnam ; Ebner, Martin ; Holzinger, Andreas. / Insights into Learning Competence Through Probabilistic Graphical Models. 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. editor / Andreas Holzinger ; Peter Kieseberg ; A Min Tjoa ; Edgar Weippl. Cham : Springer International Publishing AG , 2019. pp. 250-271 (Lecture Notes in Computer Science).
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