자료유형  

 학위논문

Control Number  

0016934367

International Standard Book Number  

9798380606882

Dewey Decimal Classification Number  

371

Main Entry-Personal Name  

Zlatkovic, Kristine.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Florida., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(227 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

General Note  

Advisor: Antonenko, Pavlo.

Dissertation Note  

Thesis (Ph.D.)--University of Florida, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약New forms of visualizations are transforming how people interact with data. This dissertation explored how undergraduates learn with infographics. The following questions guided this research:(i) What do we know about the factors influencing the processing of data visualizations?(ii) How do task-level and learner-level characteristics impact the visual processing and comprehension of infographics?(iii) Can machine learning be used to reliably predict the visual processing and comprehension of infographics using task-level and learner-level characteristics?Systematic review of the literature has shown that data visualization comprehension includes perceptive and conceptual processes, which are influenced by learning task and its complexity, strategies used to convey data, individual learner differences in previous experiences, cognitive and attentional characteristics. A study was conducted with 51 undergraduates in an eye-tracking laboratory at a major southeastern university. The learning task included using infographics with verbal and visual data representations to find answers to questions of three levels of complexity. Learners' working memory, visual search and inhibitory control abilities were evaluated as measures of individual differences in cognition. The results suggest that learners become more engaged and produce slightly more accurate results when they learn using verbal infographics. Complex tasks that require learners to make inferences by connecting newly acquired knowledge with prior knowledge always produce the least accurate results. Regardless of the data representation format, learners' visuospatial working memory and goal-oriented visual search ability significantly influence comprehension. On the contrary, low verbal working memory and inhibitory control hinder the processing of verbal infographics. Further, a random forest algorithm predicted infographics comprehension with 88.04% accuracy with learner-level and task-level characteristics contributing to the predictive performance of the model. Learners' visual processing was explored using gaze-based saliency maps. Machine learning generated less than 50% accuracy using saliency map predictions. Yet, statistical tests revealed that both task-level and learner-level characteristics are significantly associated with saliency maps. This study implies that machine learning and the proposed saliency maps may contribute to the development of adaptive infographics based on learner-level and task-level characteristics. This study contributes insights to existing knowledge on data visualization comprehension and proposes new approaches to enhance learning with information visualizations.

Subject Added Entry-Topical Term  

Educational technology.

Subject Added Entry-Topical Term  

Education.

Index Term-Uncontrolled  

Adaptive technologies

Index Term-Uncontrolled  

Data visualizations

Index Term-Uncontrolled  

Eye-tracking

Index Term-Uncontrolled  

Infographics

Index Term-Uncontrolled  

Saliency maps

Added Entry-Corporate Name  

University of Florida Curriculum and Instruction (ISC)

Host Item Entry  

Dissertations Abstracts International. 85-04A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641712