[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126206-en":3,"doc-seo-126206-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126206,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Identification of Demographic Factors Affecting Student Performance using Tree-Based Machine Learning Models - Research Paper Summary","Study identifies academic and demographic factors influencing student performance in the Logic and Set Theory course, examining effects of different learning modes during and after the COVID-19 pandemic. Quantitative exploratory research uses students from 2020–2023 cohorts at Sanata Dharma University, combining academic records (exam/assignment scores and outcomes) with questionnaire-based demographics such as parental education, income, region, gender, and high school major. Data preprocessing includes robust scaling, SMOTE for imbalance, and model tuning via GridSearchCV.","Identification of Demographic Factors Affecting Student Performance using Tree-Based Machine Learning Models  \nChatarina Enny Murwaningtyas  \nDepartment of Mathematics Education, Universitas Sanata Dharma, Yogyakarta, Indonesia  \n[enny@usd.ac.id](enny@usd.ac.id)  \n\n| ABSTRACT |  |  |\n| --- | --- | --- |\n| Article History:\u003Cbr>Received : 27-12-2024\u003Cbr>Revised : 31-03-2025\u003Cbr>Accepted : 03-04-2025\u003Cbr>Online : 26-04-2025\u003Cbr>Keywords:\u003Cbr>Demographic Factors; Academic Performance; Educational Data Mining;\u003Cbr>Tree-Based.\u003Cbr> | This study aims to identify key academic and demographic factors influencing student performance in the Logic and Set Theory course, particularly in the context of different learning modes during and after the COVID-19 pandemic. It adopts a quantitative exploratory design involving students from the 2020 to 2023 cohorts at Sanata Dharma University. Academic data (exam and assignment scores, course outcomes) and demographic data (e.g., parental education and income, region of origin, gender, and high school major) were collected from the academic system and supplemented via questionnaires. The dataset was cleaned, encoded, and normalized using RobustScaler, with class imbalance addressed through SMOTE. Descriptive statistics were used to explore initial data characteristics. Five treebased machine learning models, Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost, were implemented within a pipeline that included preprocessing and model optimization using GridSearchCV with 5-fold crossvalidation. Model evaluation employed multiple metrics, including accuracy, precision, recall, F1-score, AUC, and Average Precision. Results showed that XGBoost and CatBoost achieved the best performance (accuracy 92%, AUC 0.99) with balanced precision and recall across all four performance categories. Feature importance analysis indicated that exam and assignment scores were the strongest predictors, while demographic factors such as enrollment year, parental education, and income contributed moderately. Variables like gender, region, and high school major had minimal influence. This research demonstrates how machine learning can effectively integrate academic and demographic data, rather than analyzing them in isolation, to uncover nuanced patterns in student achievement. The findings support the development of data-driven educational interventions, such as preparatory learning modules, peer mentoring for underperforming groups, targeted academic advising for students from low-income or less-educated families, and flexible instructional strategies for cohorts affected by pandemicrelated disruptions. |  |\n|  |  |  |\n| [https://doi.org/10.31764/jtam.v9i2.28815](https://doi.org/10.31764/jtam.v9i2.28815) This is an open-access article under the CC–BY-SA license |  |  |\n\n—————————— 􀂋 ——————————  \nA. INTRODUCTION  \nStudent graduation is a crucial indicator of the success of higher education institutions and reflects their ability to educate and support students in their studies. A high graduation rate is often regarded as a marker of quality and competitiveness, demonstrating an institution's success in guiding students to complete their programs within the expected timeframe (Alhazmi & Sheneamer, 2023) . Furthermore, student success during the first semester plays a significant role in determining educational persistence (Gil et al., 2021) . Early academic performance not only reflects students' adaptation to the higher education environment, but also serves as an indicator of long-term academic success. Therefore, understanding the factors  \nthat influence students' academic achievement from the beginning of their studies is critical to supporting their educational continuity.  \nVarious studies indicate that academic success and student graduation are influenced by a complex interplay of factors, including personal characteristics (Molnár & Kocsis, 2024), socioeconomic status (Bayirli et al., 2023), and demographics (Yusof","cbCaivMWR5AQ1HLu","https://ap.wps.com/l/cbCaivMWR5AQ1HLu","pdf",1147824,1,15,"English","en",105,"# Abstract\n# Introduction\n## Background and importance of early academic performance\n## Role of demographic and socioeconomic factors\n## Impact of learning mode changes during COVID-19\n# Methodology (overview)\n## Data sources and cohort selection\n## Preprocessing, encoding, and imbalance handling\n## Tree-based models and evaluation metrics\n# Results (overview)\n## Best-performing models and predictive features\n## Effect strength across performance categories\n# Discussion and implications","[{\"question\":\"Which demographic factors and academic variables were used to predict student performance?\",\"answer\":\"Academic inputs included exam and assignment scores and course outcomes. Demographic inputs included parental education and income, region of origin, gender, enrollment year, and high school major.\"},{\"question\":\"How were class imbalance and model tuning handled?\",\"answer\":\"The study cleaned and preprocessed the dataset, then applied SMOTE to address class imbalance. Model optimization used a pipeline with GridSearchCV and 5-fold cross-validation.\"},{\"question\":\"Which tree-based models performed best and what features mattered most?\",\"answer\":\"XGBoost and CatBoost achieved the best performance, with top accuracy and near-perfect AUC. Feature importance analysis showed exam and assignment scores as the strongest predictors, while demographic factors contributed more moderately.\"}]","Identification of Demographic Factors Affecting Student Performance using Tree-Based Machine Learning Models - Research Paper Summary | PDF",1785903793,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"identification-of-demographic-factors-affecting-student-performance-using-tree-based-machine-learning-models-research-paper-summary","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-demographic-factors-affecting-student-performance-using-tree-based-machine-learning-models-research-paper-summary/126206/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which demographic factors and academic variables were used to predict student performance?","Question",{"text":76,"@type":77},"Academic inputs included exam and assignment scores and course outcomes. Demographic inputs included parental education and income, region of origin, gender, enrollment year, and high school major.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were class imbalance and model tuning handled?",{"text":81,"@type":77},"The study cleaned and preprocessed the dataset, then applied SMOTE to address class imbalance. Model optimization used a pipeline with GridSearchCV and 5-fold cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which tree-based models performed best and what features mattered most?",{"text":85,"@type":77},"XGBoost and CatBoost achieved the best performance, with top accuracy and near-perfect AUC. 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