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RIO ANDRIYAT KRISDIAWAN
Fakultas Ilmu Komputer Universitas Kuningan
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Journal : bulletin of intelligent machines and algorithms

 1 
Preprocessing and Feature Engineering of Gameplay Logs for Adaptive Mathematics Learning Dataset Construction Rio Andriyat Krisdiawan; Dede Husen; Heri Herwanto
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.23

Abstract

This study reports the preprocessing and feature engineering of gameplay logs collected from a first-grade elementary mathematics educational game prototype. The study aimed to transform raw gameplay activity records into structured analytical datasets that can support early performance description and serve as an initial data preparation stage for future adaptive mathematics learning research. A limited trial was conducted with nine first-grade students who played six sequential game levels covering early numeracy topics, including counting, number ordering, number reading, place value, and mixed review. The gameplay logs captured event-based student interactions, including session identity, level, question, selected answer, correctness status, attempt count, help usage, response time, score, and event type. The data processing workflow included data validation, cleaning, anonymization, data type handling, event filtering, feature engineering, dataset aggregation, and descriptive analysis. The preprocessing stage produced 262 clean gameplay log records consisting of 171 answer events, 28 help events, 54 level completion events, and nine game completion events. Feature engineering generated analytical indicators such as level accuracy, average response time, total help usage, average attempt count, performance category, student state, and initial adaptation action. The final outputs were organized into answer-level, level-level, student-level, and adaptive feature datasets. The anonymized dataset and data dictionary are provided as supplementary materials to support reproducibility and future reuse. The results indicate that raw gameplay logs can be converted into structured datasets for early learning analytics and preliminary adaptive learning data preparation, without making claims about learning effectiveness or final adaptive model performance.
Co-Authors . Wachjuni A.A. Ketut Agung Cahyawan W Abda, Abda Naufal Zabbar Ade Kurniawan Agris Kurniawan Aji Permana Aji Permana, Aji Amalia Asikin, Nida Amir Hamzah Aysyah Noor Shobah Azahra, Nur Anisa Darsanto Dede Husen Dede Irawan Dede Sunarto Dessi Hermawaty Dias Eka Prayuda Diki Pahrilah Elda Candra Galih Elvan Praramdani Encep Sayid Amrulloh Erik Kurniadi Erlan Darmawan fitra imaniar Haqmi, Fahrul Heri Herwanto Heru Budianto Herwanto, Heri Ikhsan Khaeruddin Indraguna BAS Iwan Lesmana Iwan Lesmana Kusumo Setyo Aryogi Laksono Trisnantoro Lili Karmela Fitriani Lutfi Rohmawati Maulana, Arip Rahman Mochamad Anggi Zaelani Mughits, Muhammad Choirul Muhammad Abd Azis Muhammad Abdullah Fadli Muhammad Bahrul Lutfianto Nida Amalia Asikin Nida Amalia Nasikin Nugraha, Gugun Rizal Nunu Nugraha Nunu Nugraha Nur Alamsyah Nura'isyah, Ninda Pratama, Asep Sabilah Ramdoni Ramdoni Rasul, Muh Abi Regita Novianti Ridwan Alamsyah Rifky Putra Pratama Rio Priantama Rio Priantama Rohmana, Muhamad Faza Rosman, Yoga Febriana Sanhari, Yogi sherly gina supratman Sugeng Supriyadi supratman, sherly gina Suseno, Endra Sutarbi, Tata Tata Sutabri Tito Sugiharto Tito Sugiharto Tri Ramdhany Yanti, Pani Puja Yasir Maulana Yati Nurhayati Yohanes Bowo Widodo Yulyanto Yulyanto Zafira Fatsya