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20260401T092057Z
DTSTART;TZID=America/New_York:20260511T100000
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 0260511T110000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/compu
 ter-science-department-phd-dissertation-defense-yiren-ding-assessments-mod
 eling-and-platforming
Computer Science Department , PhD Dissertation Defense , Yiren Ding "   Assessments, Modeling, and Platforming for Data Visualization Literacy"
\nYiren Ding\nPhD Candidate\nWPI – Computer Science Department\nMonday, May 11th 2026\nTime:
  10:00 AM – 11:30AM\nLocation: Fuller Labs 141\nZoom Link:https://wpi.zo
 om.us/j/96907674648\nCommittee members :\nLane Harrison (Advisor, Computer
  Science, WPI)\nErin Solovey(Computer Science, WPI)\nStacy Shaw (Learning 
 Science \& Technology, WPI)\nAlexander Lex (External, Computer Science, Un
 iversity of Utah \& TU Graz)\nAbstract:\nData visualization communicates i
 nformation through graphical representations, enabling efficient data expl
 oration and the discovery of insights. Data visualization literacy, the ab
 ility to interpret and reason with visualized data, is an important cognit
 ive skill for decision making, communication, and information exploration.
  However, measuring and improving visualization literacy remains an underd
 eveloped challenge.\nEmpirical studies provide a promising way to understa
 nd visualization literacy, but they are often difficult to conduct. Visual
 ization stimuli are complex and experimental procedures can vary widely. I
 n addition, traditional analyses that rely on the “average observer” o
 ften overlook meaningful differences between individuals, making it diffic
 ult to build models that support individual performance.\nThis dissertatio
 n addresses these challenges through four research directions. First, it e
 xpands visualization literacy experiments and modeling approaches. Second,
  it designs assessments and interventions to improve individual visualizat
 ion literacy. Third, it develops a framework that supports researchers in 
 building flexible visualization empirical studies. Fourth, it designs and 
 evaluates an educational platform that promotes visualization literacy.\nT
 he results advance modeling of individual visualization literacy, explore 
 performance modeling in motion based visual channels, introduce feedback m
 echanisms for visualization experiments, provide tools that simplify empir
 ical study development, and demonstrate the potential of online platforms 
 for supporting visualization literacy learning.\n
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