[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126078-en":3,"doc-seo-126078-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126078,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for Detecting Gender Bias at Chalmers - Using Course Evaluations to Fight the Patriarchy","This thesis studies gender bias in course evaluations using machine learning and NLP. It analyzes differences in what students write depending on the gender of the examiner, and complements text-based exploration with traditional statistical methods. The work examines links between examiner gender, the proportion of female students, grading outcomes, and teaching language. Findings indicate that courses with female examiners receive lower overall impression scores, Swedish-taught courses score lower than English ones, and word patterns are clearer when predicting the comment author’s gender.","Machine Learning for Detecting Gender Bias at Chalmers  \nUsing Course Evaluations to Fight the Patriarchy  \nMaster’s thesis in Computer science and engineering LINNEA NILSSON  \nSARAH LINDAU  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2023  \nMaster’s Thesis 2023  \nMachine Learning for Detecting Gender Bias at  \nChalmers  \nUsing Course Evaluations to Fight the Patriarchy  \nLinnea Nilsson Sarah Lindau  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2023  \nMachine Learning for Detecting Gender Bias in Course Evaluations Using Course Evaluations to Fight the Patriarchy  \nLinnea Nilsson Sarah Lindau  \n© Linnea Nilsson and Sarah Lindau, 2023 .  \nSupervisor: Peter Ljunglöf, Department of Computer Science and Engineering Examiner: Moa Johansson, Department of Computer Science and Engineering  \nMaster’s Thesis 2023  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nCover: A statue of a woman in an elbow stand on top of various literature from courses at Chalmers. The picture is taken by Sarah Lindau.  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nMachine Learning for Detecting Gender Bias in Course Evaluations Using Course Evaluations to Fight the Patriarchy  \nLinnea Nilsson Sarah Lindau  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nThis thesis studies gender bias in course evaluations through the lens of machine learning and NLP. Different methods are used to examine and explore the data and find differences in what students write about courses depending on the gender of the examiner. The data is also examined using more traditional statistical methods to get an understanding of how the students’ impressions of the courses are related to the gender of the examiner. Other aspects related to gender and gender bias are also examined, such as how the proportion of female students relates to the gender of the examiner and whether male or female examiners give different grades to their students. Student grades and teaching language are also factors that are being examined to see whether there is any bias against female examiners or students that is easily detectable in the data. The main findings are that courses with female examiners seem to get lower overall impression scores than those with male examiners. Courses taught in Swedish also receive lower scores, compared to the English courses. No clear patterns as to what words are used when writing comments about a course with a male or female examiner were found. When trying to predict the author gender the patterns were clearer, finding that men write more words directly related to the course and women write more words related to communication.  \nSammanfattning  \nEn masteruppsats som studerar könsbias i kursvärderingar med hjälp av maskininlärningsmetoder och NLP. Sammantaget undersöks datan på olika sätt för att se om några tecken på bias mot kvinnliga examinatorer eller studenter är lätta att hitta. En stor del av arbetet går ut på att träna olika maskininlärningsmodeller på textkommentarer för att ta reda på vilka ord som är viktiga för att förutsäga hur stor del kvinnor det är bland eleverna i en kurs och vilket kön examinatorn har. Med mer traditionella metoder undersöks huruvida kvinnliga och manliga examinatorerger olika betyg till sina elever beroende på elevens kön och om språket som kursen hålls på har någon betydelse för resultaten. De primära resultaten är att kurser med kvinnlig examinator får lägre betyg i helhetsbedömningen än de kurser med manlig examinator. Kurer som lärs ut på svenska får också lägre resultat i bedömningen, jämfört med engelska kurser. Inga tydliga mönster för hur studenter skriver om kurser med kvinnl","cbCaia9jsjMDpMel","https://ap.wps.com/l/cbCaia9jsjMDpMel","pdf",4216406,6,1,120,"English","en",105,"# 1 Introduction\n## 1.1 Gender Issues in Academia\n## 1.2 Gender Bias against teachers in Course Evaluations\n## 1.3 Problem Description\n# 2 Theory\n## 2.1 Sex and Gender\n## 2.2 Gender Bias\n## 2.3 Courses and Grading at Chalmers\n## 2.4 The Course Evaluation Process at Chalmers\n## 2.5 Preparation of Data for Machine Learning\n## 2.6 Machine Learning\n# 3 Methods\n## 3.1 Research Questions\n## 3.2 Description of the Data\n## 3.3 Data Selection\n## 3.4 Statistical Analysis of the Data\n## 3.5 Examiner Gender Prediction\n## 3.6 Comment Author Gender Prediction\n# 4 Results\n## 4.1 Statistical Analysis of the Data\n## 4.2 Examiner Gender Prediction\n## 4.3 Comment Author Gender Prediction","[{\"question\":\"How does the thesis use machine learning to study gender bias in course evaluations?\",\"answer\":\"It applies machine learning and NLP to text comments, comparing what students write based on the examiner’s gender. It also uses statistical analysis to relate impressions to examiner gender.\"},{\"question\":\"What role does teaching language play in the results?\",\"answer\":\"Courses taught in Swedish receive lower overall scores than English courses, according to the findings reported in the abstract.\"},{\"question\":\"Can the thesis predict the gender of the comment author, and what word patterns emerge?\",\"answer\":\"Yes. The prediction shows clearer patterns: men use more words directly related to the course content, while women use more words related to communication.\"}]","Machine Learning for Detecting Gender Bias at Chalmers - Using Course Evaluations to Fight the Patriarchy | PDF",1785902963,302,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-for-detecting-gender-bias-at-chalmers-using-course-evaluations-to-fight-the-patriarchy","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-for-detecting-gender-bias-at-chalmers-using-course-evaluations-to-fight-the-patriarchy/126078/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"How does the thesis use machine learning to study gender bias in course evaluations?","Question",{"text":77,"@type":78},"It applies machine learning and NLP to text comments, comparing what students write based on the examiner’s gender. It also uses statistical analysis to relate impressions to examiner gender.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What role does teaching language play in the results?",{"text":82,"@type":78},"Courses taught in Swedish receive lower overall scores than English courses, according to the findings reported in the abstract.",{"name":84,"@type":75,"acceptedAnswer":85},"Can the thesis predict the gender of the comment author, and what word patterns emerge?",{"text":86,"@type":78},"Yes. The prediction shows clearer patterns: men use more words directly related to the course content, while women use more words related to communication.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"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":108,"slug":139},19,"General","general"]