Exploring wordle: a data-driven analysis of gameplay dynamics, mathematical modeling, and educational implications

Shuxin Wang; Guohao Zhou; Zongxuan Zhang; Jianzheng Liu

doi:10.1117/12.3036932

21 July 2024 Exploring wordle: a data-driven analysis of gameplay dynamics, mathematical modeling, and educational implications

Shuxin Wang, Guohao Zhou, Zongxuan Zhang, Jianzheng Liu

Author Affiliations +

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132190F (2024) https://doi.org/10.1117/12.3036932
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

The study delves into the analysis of the popular Wordle puzzle game, leveraging data collected on Twitter to establish mathematical models. By examining Wordle players' behaviors, including strategy choices, word guessing frequency, and potential challenge modes, valuable insights are gained to optimize game design for an enhanced user experience. The research also explores the game's potential educational application, specifically as a tool for English vocabulary learning. Addressing the first question, an ARIMA time series model was employed to predict the number of reports on March 1, 2023. The model, refined by outlier removal and expert modeler matching, forecasts the final number of reports within the range of [11953, 17730], validated through residual ACF, PACF, and white noise tests. For the second question, word attributes were categorized into three indicators: part of speech, letter occurrence statistics, and the frequency of the five most common letters in the Oxford Dictionary. Pearson correlation coefficients revealed a general correlation between part of speech and difficulty mode (0.2774) and between high-frequency letter occurrence and difficulty mode (0.2093). These findings were further validated through a Pearson correlation coefficient hypothesis test. Future research will focus on optimizing ARIMA model parameters for increased accuracy, robustness, and generalization, while exploring additional word attributes to uncover more connections between game difficulty and word characteristics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuxin Wang, Guohao Zhou, Zongxuan Zhang, and Jianzheng Liu "Exploring wordle: a data-driven analysis of gameplay dynamics, mathematical modeling, and educational implications", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132190F (21 July 2024); https://doi.org/10.1117/12.3036932

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KEYWORDS

Correlation coefficients

Data modeling

Autoregressive models

Analytical research

Design

Mathematical modeling

Statistical modeling

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