[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128540-en":3,"doc-seo-128540-105":31,"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128540,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fairness-aware Machine Learning in Educational Data Mining - Abstract","Fairness is a core requirement of educational systems, and it becomes critical as artificial intelligence and machine learning increasingly drive decisions in education. Educational data mining applies data mining and ML to tasks such as student performance prediction and student grouping, but ML outcomes may rely on protected attributes like race or gender, causing discrimination. Bias can also originate from learning-environment data, motivating bias-aware exploratory analysis. This thesis proposes methods to mitigate discriminatory outcomes via bias-aware analysis, fairness measure evaluation, and fair-capacitated grouping approaches.","FAIRNESS-AWARE MACHINE LEARNING IN EDUCATIONAL  \nDATA MINING  \nVon der Fakult¨at f¨ur Elektrotechnik und Informatik der Gottfried Wilhelm Leibniz Universit¨at Hannover zur Erlangung des akademischen Grades  \nDOKTOR DER NATURWISSENSCHAFTEN  \nDr. rer. nat.  \ngenehmigte Dissertation  \nvon  \nM. Sc. Tai Le Quy  \ngeboren am 03 September 1985, in Hanam, Vietnam  \nReferentin: Prof. Dr. Eirini Ntoutsi Korreferent: Prof. Dr. Gunnar Friege Vorsitz: Prof. Dr. Johannes Krugel  \nTag der Promotion: 16.10.2023  \nDECLARATION OF AUTHORSHIP  \nI, Tai Le Quy, declare that this thesis, titled “Fairness-aware Machine Learning in Educational Data Mining” and the work presented in it are my own. I confirm that:  \n• This work was done wholly or mainly while in candidature for a research degree at this university.  \n• Where any part of this thesis has previously been submitted for a degree or any other qualification at this university or any other institution, this has been clearly stated.  \n• Where I have consulted the published work of others, this is always clearly attributed.  \n• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n• I have acknowledged all main sources of help.  \n• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nABSTRACT  \nFairness is an essential requirement of every educational system, which is reflected ina variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions:  \n• We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a director indirect relationship with the protected attributes w.r.t. prediction outcomes.  \n• We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects.  \n• We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering.  \n• We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students’ preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding t","cbCaivYxhy88G4OL","https://ap.wps.com/l/cbCaivYxhy88G4OL","pdf",11707161,2,1,162,"English","en",105,"# Abstract\n## Bias-aware exploratory educational data analysis\n## Evaluation of group fairness measures\n## Fair-capacitated clustering for collaborative learning\n## Multi-fair capacitated student-topic grouping","[{\"question\":\"Why is fairness a key requirement in educational machine learning?\",\"answer\":\"Educational decisions increasingly use AI/ML, and ML-based decisions can rely on protected attributes such as race or gender, leading to discrimination. Ensuring fairness also supports equity within educational systems.\"},{\"question\":\"What bias-related analysis does the thesis propose for educational datasets?\",\"answer\":\"It performs bias-aware exploratory analysis using Bayesian networks to identify relationships among attributes. The analysis targets features that have a direct or indirect relationship with protected attributes with respect to prediction outcomes.\"},{\"question\":\"How does the thesis address student grouping while respecting fairness?\",\"answer\":\"It introduces fair-capacitated clustering for collaborative learning, considering cluster fairness and cluster cardinalities. It also proposes multi-fair capacitated (MFC) student-topic grouping that satisfies student preferences, balances group sizes, and maximizes protected-attribute diversity using three algorithmic approaches.\"}]","Fairness-aware Machine Learning in Educational Data Mining - Abstract | PDF",1786001624,408,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"fairness-aware-machine-learning-in-educational-data-mining-abstract","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/fairness-aware-machine-learning-in-educational-data-mining-abstract/128540/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is fairness a key requirement in educational machine learning?","Question",{"text":76,"@type":77},"Educational decisions increasingly use AI/ML, and ML-based decisions can rely on protected attributes such as race or gender, leading to discrimination. Ensuring fairness also supports equity within educational systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What bias-related analysis does the thesis propose for educational datasets?",{"text":81,"@type":77},"It performs bias-aware exploratory analysis using Bayesian networks to identify relationships among attributes. The analysis targets features that have a direct or indirect relationship with protected attributes with respect to prediction outcomes.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis address student grouping while respecting fairness?",{"text":85,"@type":77},"It introduces fair-capacitated clustering for collaborative learning, considering cluster fairness and cluster cardinalities. It also proposes multi-fair capacitated (MFC) student-topic grouping that satisfies student preferences, balances group sizes, and maximizes protected-attribute diversity using three algorithmic approaches.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]