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Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there i...

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Autores principales: Li, Xiang, Younes, Rabih, Bairaktarova, Diana, Guo, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180473/
https://www.ncbi.nlm.nih.gov/pubmed/32244360
http://dx.doi.org/10.3390/s20071949
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author Li, Xiang
Younes, Rabih
Bairaktarova, Diana
Guo, Qi
author_facet Li, Xiang
Younes, Rabih
Bairaktarova, Diana
Guo, Qi
author_sort Li, Xiang
collection PubMed
description The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.
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spelling pubmed-71804732020-05-01 Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data Li, Xiang Younes, Rabih Bairaktarova, Diana Guo, Qi Sensors (Basel) Article The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level. MDPI 2020-03-31 /pmc/articles/PMC7180473/ /pubmed/32244360 http://dx.doi.org/10.3390/s20071949 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Xiang
Younes, Rabih
Bairaktarova, Diana
Guo, Qi
Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title_full Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title_fullStr Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title_full_unstemmed Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title_short Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
title_sort predicting spatial visualization problems’ difficulty level from eye-tracking data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180473/
https://www.ncbi.nlm.nih.gov/pubmed/32244360
http://dx.doi.org/10.3390/s20071949
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