Expertise Prediction of Tetris Players Using Eye Tracking Information

Stijn Rotman; Gianluca Guglielmo; Boris Čule; Michał Klincewicz

doi:10.1007/978-3-031-91398-3_23

Expertise Prediction of Tetris Players Using Eye Tracking Information

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
Editors	Georg Kremp, Kai Puolamäki , Ioanna Miliou
Publisher	Springer Cham
Pages	305–317
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-031-91398-3_23
Publication status	Published - 3 Feb 2025
Event	23rd International Symposium on Intelligent Data Analysis - Konstanz, Germany Duration: 7 May 2025 → 9 May 2025 Conference number: 23

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer, Cham
Volume	15669

Conference

Conference	23rd International Symposium on Intelligent Data Analysis
Abbreviated title	IDA 2025
Country/Territory	Germany
City	Konstanz
Period	7/05/25 → 9/05/25

Keywords

Time series classification
Expertise
Eye tracking

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10.1007/978-3-031-91398-3_23

Expertise Prediction of Tetris Players Using Eye Tracking InformationSubmitted manuscript, 5 MBLicence: CC BY
Expertise Prediction of Tetris Players Using Eye Tracking InformationAccepted author manuscript, 5 MBLicence: Other

Cite this

Rotman, S., Guglielmo, G., Čule, B., & Klincewicz, M. (2025). Expertise Prediction of Tetris Players Using Eye Tracking Information. In G. Kremp, K. Puolamäki , & I. Miliou (Eds.), Advances in Intelligent Data Analysis XXIII: 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings (pp. 305–317). (Lecture Notes in Computer Science; Vol. 15669). Springer Cham. https://doi.org/10.1007/978-3-031-91398-3_23

Rotman, Stijn ; Guglielmo, Gianluca ; Čule, Boris et al. / Expertise Prediction of Tetris Players Using Eye Tracking Information. Advances in Intelligent Data Analysis XXIII: 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings. editor / Georg Kremp ; Kai Puolamäki ; Ioanna Miliou. Springer Cham, 2025. pp. 305–317 (Lecture Notes in Computer Science).

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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",

doi = "10.1007/978-3-031-91398-3\_23",

language = "English",

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editor = "\{ Kremp\}, \{Georg \} and \{Puolam{\"a}ki \}, \{ Kai \} and Miliou, \{Ioanna \}",

booktitle = "Advances in Intelligent Data Analysis XXIII",

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Rotman, S , Guglielmo, G , Čule, B & Klincewicz, M 2025, Expertise Prediction of Tetris Players Using Eye Tracking Information. in G Kremp, K Puolamäki & I Miliou (eds), Advances in Intelligent Data Analysis XXIII: 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Konstanz, Germany, May 7-9, 2025, Proceedings. Lecture Notes in Computer Science, vol. 15669, Springer Cham, pp. 305–317, 23rd International Symposium on Intelligent Data Analysis, Konstanz, Germany, 7/05/25. https://doi.org/10.1007/978-3-031-91398-3_23

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. ed. / Georg Kremp; Kai Puolamäki ; Ioanna Miliou. Springer Cham, 2025. p. 305–317 (Lecture Notes in Computer Science; Vol. 15669).

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

N1 - Conference code: 23

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.

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KW - Eye tracking

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BT - Advances in Intelligent Data Analysis XXIII

A2 - Kremp, Georg

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A2 - Miliou, Ioanna

PB - Springer Cham

T2 - 23rd International Symposium on Intelligent Data Analysis

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ER -

Rotman S , Guglielmo G , Čule B , Klincewicz M. Expertise Prediction of Tetris Players Using Eye Tracking Information. In Kremp G, Puolamäki K, Miliou I, editors, 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. p. 305–317. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-91398-3_23

Expertise Prediction of Tetris Players Using Eye Tracking Information

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