Expertise Prediction of Tetris Players Using Eye Tracking Information

Stijn Rotman; Gianluca Guglielmo; Boris Čule; Michał Klincewicz

Expertise Prediction of Tetris Players Using Eye Tracking Information

Stijn Rotman, Gianluca Guglielmo, Boris Čule, Michał Klincewicz

Cognitive Science and AI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

We report on an application of three multivariate time series classification methods, Hive-Cote 2.0, MiniRocket, and Mr-Petsc, to gaze and eyelid movement data to classify expertise. Our methods can be used to noninvasively monitor performance and identify experts using low-grade equipment. The test case was Tetris, which is a video game in which players arrange falling blocks to clear horizontal lines with increasing points and difficulty as the game advances. In addition to being able to classify the expert players, we can attribute the patterns within a time series that led to a prediction with Mr-Petsc. This allows us to describe eye behavior that is associated with expertise. This method can be used in any performance classification that involves screen-based activity that is accompanied by eye movement recordings, for example with a webcam.

Original language	English
Title of host publication	Advances in Intelligent Data Analysis XXIII
Subtitle of host publication	23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings
Publisher	Springer Cham
Publication status	Accepted/In press - 3 Feb 2025

Keywords

Time series classification
Expertise
Eye tracking

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Expertise Prediction of Tetris Players Using Eye Tracking Information
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Cite this

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title = "Expertise Prediction of Tetris Players Using Eye Tracking Information",

abstract = "We report on an application of three multivariate time series classification methods, Hive-Cote 2.0, MiniRocket, and Mr-Petsc, to gaze and eyelid movement data to classify expertise. Our methods can be used to noninvasively monitor performance and identify experts using low-grade equipment. The test case was Tetris, which is a video game in which players arrange falling blocks to clear horizontal lines with increasing points and difficulty as the game advances. In addition to being able to classify the expert players, we can attribute the patterns within a time series that led to a prediction with Mr-Petsc. This allows us to describe eye behavior that is associated with expertise. This method can be used in any performance classification that involves screen-based activity that is accompanied by eye movement recordings, for example with a webcam.",

keywords = "Time series classification, Expertise, Eye tracking",

author = "Stijn Rotman and Gianluca Guglielmo and Boris {\v C}ule and Micha{\l} Klincewicz",

year = "2025",

month = feb,

day = "3",

language = "English",

booktitle = "Advances in Intelligent Data Analysis XXIII",

publisher = "Springer Cham",

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Expertise Prediction of Tetris Players Using Eye Tracking Information. / Rotman, Stijn ; Guglielmo, Gianluca ; Čule, Boris et al.
Advances in Intelligent Data Analysis XXIII: 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings. Springer Cham, 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Expertise Prediction of Tetris Players Using Eye Tracking Information

AU - Rotman, Stijn

AU - Guglielmo, Gianluca

AU - Čule, Boris

AU - Klincewicz, Michał

PY - 2025/2/3

Y1 - 2025/2/3

N2 - We report on an application of three multivariate time series classification methods, Hive-Cote 2.0, MiniRocket, and Mr-Petsc, to gaze and eyelid movement data to classify expertise. Our methods can be used to noninvasively monitor performance and identify experts using low-grade equipment. The test case was Tetris, which is a video game in which players arrange falling blocks to clear horizontal lines with increasing points and difficulty as the game advances. In addition to being able to classify the expert players, we can attribute the patterns within a time series that led to a prediction with Mr-Petsc. This allows us to describe eye behavior that is associated with expertise. This method can be used in any performance classification that involves screen-based activity that is accompanied by eye movement recordings, for example with a webcam.

AB - We report on an application of three multivariate time series classification methods, Hive-Cote 2.0, MiniRocket, and Mr-Petsc, to gaze and eyelid movement data to classify expertise. Our methods can be used to noninvasively monitor performance and identify experts using low-grade equipment. The test case was Tetris, which is a video game in which players arrange falling blocks to clear horizontal lines with increasing points and difficulty as the game advances. In addition to being able to classify the expert players, we can attribute the patterns within a time series that led to a prediction with Mr-Petsc. This allows us to describe eye behavior that is associated with expertise. This method can be used in any performance classification that involves screen-based activity that is accompanied by eye movement recordings, for example with a webcam.

KW - Time series classification

KW - Expertise

KW - Eye tracking

M3 - Conference contribution

BT - Advances in Intelligent Data Analysis XXIII

PB - Springer Cham

ER -

Expertise Prediction of Tetris Players Using Eye Tracking Information

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