Classification of Visual Strategies in Physics Vector Field Problem-solving

Saleh Mozaffari, Mohammad Al-Naser, Pascal Klein, Stefan Küchemann, Jochen Kuhn, Thomas Widmann, Andreas Dengel

Abstract

In this study, we taught 20 physics students two different visual strategies to graphically interpret the physical meaning of vector field divergence. Using eye-tracking technology, we recorded students’ eye-movement behavior of both strategies when they were engaged in graphical vector field representations. From the eye-tracking data we extracted the number of fixations and saccadic direction and proposed a linear SVM model to classify strategies of problem-solving in the vector field domain. The results show different gaze patterns for the two strategies, and the influence of vector flow orientation on gaze-patterns. A high accuracy of 81.2%(0.11%) has been achieved by testing the algorithm using cross-validation, i.e. that the algorithm is able to predict the strategy the student applies to judge the divergence of a vector field. The results provide guiding tools for learning-effective instruction design and teachers gain benefit from monitoring the students’ non-verbal level of performance and fluency using each strategy. Apart from that, students would receive the objective feedback on their progress of learning.

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Paper Citation


in Harvard Style

Mozaffari S., Al-Naser M., Klein P., Küchemann S., Kuhn J., Widmann T. and Dengel A. (2020). Classification of Visual Strategies in Physics Vector Field Problem-solving.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 257-267. DOI: 10.5220/0009173902570267


in Bibtex Style

@conference{icaart20,
author={Saleh Mozaffari and Mohammad Al-Naser and Pascal Klein and Stefan Küchemann and Jochen Kuhn and Thomas Widmann and Andreas Dengel},
title={Classification of Visual Strategies in Physics Vector Field Problem-solving},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={257-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009173902570267},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Classification of Visual Strategies in Physics Vector Field Problem-solving
SN - 978-989-758-395-7
AU - Mozaffari S.
AU - Al-Naser M.
AU - Klein P.
AU - Küchemann S.
AU - Kuhn J.
AU - Widmann T.
AU - Dengel A.
PY - 2020
SP - 257
EP - 267
DO - 10.5220/0009173902570267