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PoTeC: A German naturalistic eye-tracking-while-reading corpus.

Deborah N Jakobi1, Thomas Kern2, David R Reich2

  • 1Department of Computational Linguistics, University of Zurich, Andreasstrasse 15, Zurich, 8050, Switzerland. jakobi@cl.uzh.ch.

Behavior Research Methods
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

The Potsdam Textbook Corpus (PoTeC) offers valuable eye-tracking data for studying expert and novice reading strategies. This naturalistic corpus enables research into how different academic backgrounds influence scientific text comprehension.

Keywords:
CorpusEye-trackingGermanReading

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Educational Technology

Background:

  • Understanding how individuals process scientific texts is crucial for improving educational materials and reading strategies.
  • Existing eye-tracking corpora often lack the diversity in expertise and disciplinary backgrounds needed to study nuanced reading behaviors.

Purpose of the Study:

  • To introduce the Potsdam Textbook Corpus (PoTeC), a novel dataset for eye-tracking-while-reading research.
  • To facilitate the analysis of expert versus novice reading strategies in scientific texts.
  • To provide a comprehensive resource for studying the influence of academic background on text comprehension.

Main Methods:

  • Collected naturalistic eye-tracking data from 75 participants reading 12 scientific texts.
  • Employed a 2x2x2 factorial design, manipulating participant study level, discipline, and text domain.
  • Assessed reading comprehension and domain knowledge through targeted questions.

Main Results:

  • The corpus captures eye movements of both domain experts and novices within a single study design.
  • Linguistic features are annotated at multiple levels, enabling detailed analysis.
  • The data allows for within-participant comparisons of reading strategies across different conditions.

Conclusions:

  • PoTeC is a unique resource for investigating expert and non-expert reading differences in scientific contexts.
  • The corpus and associated code are publicly available, promoting open science and further research.
  • Integration with the pymovements package simplifies data analysis in Python and R.