Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
| Autor: | Sandra Rodríguez Arribas, Raúl Marticorena Sánchez, María Consuelo Sáiz Manzanares, María José Zaparaín Yáñez, Gonzalo Andrés López, René Jesús Payo Hernanz, Alberto Calvo Rodríguez, Caroline Françoise Martin |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Technology Adolescent General Chemical Engineering General Biochemistry Genetics and Molecular Biology Task (project management) Learning styles Young Adult Adult education Mathematics education Data Mining Humans Eye-Tracking Technology Students Self-regulated learning Cluster analysis Aged General Immunology and Microbiology Teaching General Neuroscience Information processing Middle Aged Task analysis Eye tracking Curriculum Psychology |
| Zdroj: | Journal of Visualized Experiments. |
| ISSN: | 1940-087X |
| DOI: | 10.3791/62103 |
| Popis: | Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics. |
| Databáze: | OpenAIRE |
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