TY - GEN

T1 - Expertise estimation based on simple multimodal features

AU - Ochoa, Xavier

AU - Chiluiza, Katherine

AU - Méndez, Gonzalo

AU - Luzardo, Gonzalo

AU - Guamán, Bruno

AU - Castells, James

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - math data corpus

KW - multimodal learning analytics

UR - http://www.scopus.com/inward/record.url?scp=84892570670&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84892570670&partnerID=8YFLogxK

U2 - 10.1145/2522848.2533789

DO - 10.1145/2522848.2533789

M3 - Conference contribution

AN - SCOPUS:84892570670

SN - 9781450321297

T3 - ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction

SP - 583

EP - 590

BT - ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction

T2 - 2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013

Y2 - 9 December 2013 through 13 December 2013

ER -