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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7180473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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