{"id":{"repo_id":"st-andrews","oai_identifier":"oai:research-repository.st-andrews.ac.uk:10023/31720"},"canonical_url":"https://search.dev.ndltd.org/etd/st-andrews/oai:research-repository.st-andrews.ac.uk:10023/31720","repository":{"repo_id":"st-andrews","name":"U. of St. Andrews","base_url":"https://research-repository.st-andrews.ac.uk/dspace-oai/request"},"display":{"title":"LeeLee : an attention visualisation analysis while reading academically","abstract":"Understanding attention during academic reading is fundamental in the context of Human-Computer Interaction (HCI). Attention is a limited, yet essential resource that is crucial for processing academic texts and performing focused reading tasks. In a hyper-connected society, distractions from digital devices adversely influence specific academic reading patterns, such as sustained attention and comprehension, making it challenging to maintain focus. This study explores and addresses the need for attention in academic reading. With the increasing use of digital academic reading platforms such as e-readers, tablets, and computer screens, it is essential to understand how these environments impact attention. A major finding of this thesis is that attention while reading academically can be decoded and analysed through visualisations, thus revealing patterns of fixations, saccades, and cognitive engagement levels that inform strategies to enhance focus and efficiency. The key results show that both Eye-tracking and EEG can provide quantifiable data on attentional patterns during reading. The study used the \"Visualization as Intermediate Representations (VLAIR) technique to interpret these data. Eyetracking data revealed specific patterns of fixations, interest, and cognitive effort, while EEG data provided insights into levels of cognitive engagement through alpha and beta wave activity. Combining these methods facilitated a detailed analysis of reading behaviour, including eye movement patterns and cognitive load. The findings of this research offer practical implications for the design of digital learning platforms and interactive reading environments. By understanding and visualising attention patterns, educators and technologists could develop better strategies and tools to support focused academic reading, ultimately enhancing learning outcomes.","abstract_html":"Understanding attention during academic reading is fundamental in the context of Human-Computer Interaction (HCI). Attention is a limited, yet essential resource that is crucial for processing academic texts and performing focused reading tasks. In a hyper-connected society, distractions from digital devices adversely influence specific academic reading patterns, such as sustained attention and comprehension, making it challenging to maintain focus. This study explores and addresses the need for attention in academic reading. With the increasing use of digital academic reading platforms such as e-readers, tablets, and computer screens, it is essential to understand how these environments impact attention. A major finding of this thesis is that attention while reading academically can be decoded and analysed through visualisations, thus revealing patterns of fixations, saccades, and cognitive engagement levels that inform strategies to enhance focus and efficiency. The key results show that both Eye-tracking and EEG can provide quantifiable data on attentional patterns during reading. The study used the "Visualization as Intermediate Representations (VLAIR) technique to interpret these data. Eyetracking data revealed specific patterns of fixations, interest, and cognitive effort, while EEG data provided insights into levels of cognitive engagement through alpha and beta wave activity. Combining these methods facilitated a detailed analysis of reading behaviour, including eye movement patterns and cognitive load. The findings of this research offer practical implications for the design of digital learning platforms and interactive reading environments. By understanding and visualising attention patterns, educators and technologists could develop better strategies and tools to support focused academic reading, ultimately enhancing learning outcomes.","abstract_has_math":false,"creators":["Moreno Rocha, Mario Alberto"],"institution":"The University of St Andrews","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Ye, Juan","Nacenta, Miguel"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-03","date_published":"2025-07-03","updated_at":"2026-07-27T21:10:04Z","subjects":["Attention visualisation","Academic reading","Eye-tracking","Electroencephalography (EEG)","Human-Computer Interaction (HCI)","Reading behaviour","Machine learning","Visualization as Intermediate Representations (VLAIR)"],"languages":["en"],"rights":["Creative Commons Attribution-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17630/sta/1269"],"render_values":[{"text":"https://doi.org/10.17630/sta/1269","href":"https://doi.org/10.17630/sta/1269","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10023/31720","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ye, Juan","Nacenta, Miguel"]},{"key":"dc:creator","label":"Author","values":["Moreno Rocha, Mario Alberto"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-03-26T10:04:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-03-26T10:04:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-07-03"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["The University of St Andrews"]},{"key":"dc:relation","label":"Dc Relation","values":["Moreno Rocha, M. 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Attention is a limited, yet essential resource that is crucial for processing academic texts and performing focused reading tasks. In a hyper-connected society, distractions from digital devices adversely influence specific academic reading patterns, such as sustained attention and comprehension, making it challenging to maintain focus. This study explores and addresses the need for attention in academic reading. With the increasing use of digital academic reading platforms such as e-readers, tablets, and computer screens, it is essential to understand how these environments impact attention. A major finding of this thesis is that attention while reading academically can be decoded and analysed through visualisations, thus revealing patterns of fixations, saccades, and cognitive engagement levels that inform strategies to enhance focus and efficiency. The key results show that both Eye-tracking and EEG can provide quantifiable data on attentional patterns during reading. The study used the \"Visualization as Intermediate Representations (VLAIR) technique to interpret these data. Eyetracking data revealed specific patterns of fixations, interest, and cognitive effort, while EEG data provided insights into levels of cognitive engagement through alpha and beta wave activity. Combining these methods facilitated a detailed analysis of reading behaviour, including eye movement patterns and cognitive load. The findings of this research offer practical implications for the design of digital learning platforms and interactive reading environments. By understanding and visualising attention patterns, educators and technologists could develop better strategies and tools to support focused academic reading, ultimately enhancing learning outcomes."]},{"key":"dc:title","label":"Title","values":["LeeLee : an attention visualisation analysis while reading academically"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ye, Juan","Nacenta, Miguel"],"dc:creator":["Moreno Rocha, Mario Alberto"],"dc:date.accessioned":["2025-03-26T10:04:26Z"],"dc:date.available":["2025-03-26T10:04:26Z"],"dc:date.issued":["2025-07-03"],"dc:description.abstract":["Understanding attention during academic reading is fundamental in the context of Human-Computer Interaction (HCI). Attention is a limited, yet essential resource that is crucial for processing academic texts and performing focused reading tasks. In a hyper-connected society, distractions from digital devices adversely influence specific academic reading patterns, such as sustained attention and comprehension, making it challenging to maintain focus. This study explores and addresses the need for attention in academic reading. With the increasing use of digital academic reading platforms such as e-readers, tablets, and computer screens, it is essential to understand how these environments impact attention. A major finding of this thesis is that attention while reading academically can be decoded and analysed through visualisations, thus revealing patterns of fixations, saccades, and cognitive engagement levels that inform strategies to enhance focus and efficiency. The key results show that both Eye-tracking and EEG can provide quantifiable data on attentional patterns during reading. The study used the \"Visualization as Intermediate Representations (VLAIR) technique to interpret these data. Eyetracking data revealed specific patterns of fixations, interest, and cognitive effort, while EEG data provided insights into levels of cognitive engagement through alpha and beta wave activity. Combining these methods facilitated a detailed analysis of reading behaviour, including eye movement patterns and cognitive load. The findings of this research offer practical implications for the design of digital learning platforms and interactive reading environments. By understanding and visualising attention patterns, educators and technologists could develop better strategies and tools to support focused academic reading, ultimately enhancing learning outcomes."],"dc:identifier.doi":["https://doi.org/10.17630/sta/1269"],"dc:identifier.uri":["https://hdl.handle.net/10023/31720"],"dc:language.iso":["en"],"dc:publisher.institution":["The University of St Andrews"],"dc:relation":["Moreno Rocha, M. A., Nacenta, M. A., & Ye, J. (2022). 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