Research Interests:

Research Overview

• Eye-tracking methodologies and data visualizations
• Visual information processing
• Visual complexity of scientific notation
• Machine learning
• Specifications-Mastery education

Our research is grounded in the belief that to truly enhance student learning, we must first understand how students interact with instructional materials. We use eye-tracking methodologies to investigate how students read and visually search through content, specifically focusing on how they encode and comprehend organic chemical notation.

While many approaches to improving problem solving rely on analyzing student answers after a task is completed, our work focuses on an earlier and often overlooked stage: the moment when students first encounter a problem. At this stage, they begin selecting relevant features and processing visual information to form a mental representation in working memory—a crucial foundation for successful problem solving.

We use eye-tracking technology to study visual attention during problem solving in both general and organic chemistry. Our research employs both traditional screen-based eye tracking and webcam-based tracking to examine how problem features influence where students look, for how long, and how these patterns relate to achievement outcomes.

Our lab conducts discipline-based education research (DBER) with a particular focus on chemistry education. By analyzing visual attention in conjunction with the design of learning materials, we aim to gain deeper insights into how students solve problems and how changes in presentation affect cognitive processing and learning outcomes.

Current Projects

Predictive Modeling Based on Eye-Tracking During Problem Solving
In collaboration with the Computer Science Department at Catholic University and the Chemistry Department at Columbia University, this project uses machine learning to develop predictive models of student success in general chemistry. Eye-tracking data—specifically where and how long students look at various problem elements—is combined with achievement metrics to create predictive algorithms.

Problem Solving of Visuospatial Tasks in Organic Chemistry
This study explores how the complexity of chemical notation and individual differences in visuospatial ability influence student problem solving. Eye movement data is used to analyze the cognitive demands of interpreting organic chemistry problems.

Visual Complexity Tool
This project uses machine learning to develop a tool that assigns a visual complexity score to organic bond-line structures. The goal is to create a standardized method for classifying visual stimuli in chemistry education research. The tool will be validated through eye-tracking studies and visual impression surveys.

Investigation into Classroom Practices in Organic Chemistry
(Planned for Fall 2025)
This upcoming study will explore the use of specifications-mastery grading in organic chemistry. The focus will be on how this approach impacts the affective domain—students' emotions, attitudes, motivations, and values related to learning.

Publications

Havanki, K. L. & Hansen, S. J. R. (2018). What They See Impacts the Data You Get: Selection and Design of Visual Stimuli. In J. R. VandenPlas, S. J. R. Hansen, & S. Cullipher (Eds.), Eye Tracking for the Chemistry Education Researcher. (pp. 25-52). Washington, DC: ACS Publications. DOI:10.1021/bk-2018-1292.ch003 (peer reviewed)

Havanki, K. L. (2018). Studying the Language of Organic Chemistry: Visual Processing and Practical Considerations for Eye-Tracking Research in Structural Notation. In J. R. VandenPlas, S. J. R. Hansen, & S. Cullipher (Eds.), Eye Tracking for the Chemistry Education Researcher. (pp. 183-204). Washington, DC: ACS Publications. DOI:10.1021/bk-2018-1292.ch010 (peer reviewed)

Havanki, K.L. and VandenPlas, J.R. (2014). Eye Tracking Methodology for Chemistry Education Research. In D.M. Bunce and R.S. Cole (eds.), Tools of Chemistry Education Research (191-218). Washington, DC: American Chemical Society.

Bunce, D.M, Havanki, K.L., and VandenPlas, J.R. (2008). A theory-based evaluation of POGIL workshops: providing a clearer picture of POGIL adoption. In R.S. Moog and J.N. Spencer (eds.) Process Oriented Guided Inquiry Learning (POGIL) (100-113). Washington, DC: American Chemical Society.

Bunce, D.M., VandenPlas, J.R., and Havanki, K.L. (2006). Comparing the Effectiveness on Student Achievement of a Student Response System versus Online WebCT Quizzes. Journal of Chemical Education, 83(3), 488-493.