Expertise estimation based on simple multimodal features

Xavier Ochoa, Katherine Chiluiza, Gonzalo Méndez, Gonzalo Luzardo, Bruno Guamán, James Castells

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multimodal Learning Analytics is a field that studies how to process learning data from dissimilar sources in order to automatically find useful information to give feedback to the learning process. This work processes video, audio and pen strokes information included in the Math Data Corpus, a set of multimodal resources provided to the participants of the Second International Workshop on Multimodal Learning Analytics. The result of this processing is a set of simple features that could discriminate between experts and non-experts in groups of students solving mathematical problems. The main finding is that several of those simple features, namely the percentage of time that the students use the calculator, the speed at which the student writes or draws and the percentage of time that the student mentions numbers or mathematical terms, are good discriminators be- tween experts and non-experts students. Precision levels of 63% are obtained for individual problems and up to 80% when full sessions (aggregation of 16 problems) are analyzed. While the results are specific for the recorded settings, the methodology used to obtain and analyze the features could be used to create discriminations models for other contexts.

Original languageEnglish (US)
Title of host publicationICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction
Pages583-590
Number of pages8
DOIs
StatePublished - Dec 1 2013
Event2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013 - Sydney, NSW, Australia
Duration: Dec 9 2013Dec 13 2013

Publication series

NameICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction

Other

Other2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013
CountryAustralia
CitySydney, NSW
Period12/9/1312/13/13

    Fingerprint

Keywords

  • math data corpus
  • multimodal learning analytics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

Cite this

Ochoa, X., Chiluiza, K., Méndez, G., Luzardo, G., Guamán, B., & Castells, J. (2013). Expertise estimation based on simple multimodal features. In ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction (pp. 583-590). (ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction). https://doi.org/10.1145/2522848.2533789