{"id":111792,"date":"2025-11-21T16:00:29","date_gmt":"2025-11-21T15:00:29","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=book&#038;p=111792"},"modified":"2025-11-21T16:00:30","modified_gmt":"2025-11-21T15:00:30","slug":"data-synthesis-for-fairness-audits-of-learning-analytics-algorithms","status":"publish","type":"book","link":"https:\/\/industry-science.com\/en\/book\/data-synthesis-for-fairness-audits-of-learning-analytics-algorithms\/","title":{"rendered":"Data Synthesis for Fairness Audits of Learning Analytics Algorithms"},"content":{"rendered":"\n<p><\/p>\n\n\n\n<p>AKWI-Tagungsband zur 35. AKWI-Jahrestagung. Jahrgang, 2022, Seite S. 316\u2013320<\/p>\n\n\n\n<p>The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be available (in large quantities) due to privacy concerns. Our poster shares our findings on how relational data for fairness audits of Learning Analytics systems can be synthesized from little pre-existing data, using the most promising available data synthesizers.<\/p>\n\n\n\n<p><\/p>\n<div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/fairness-audit\/\">fairness audit<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/learning-analytics\/\">learning analytics<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/synthetic-data-en\/\">synthetic data<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Data%20Synthesis%20for%20Fairness%20Audits%20of%20Learning%20Analytics%20Algorithms - https:\/\/industry-science.com\/en\/book\/data-synthesis-for-fairness-audits-of-learning-analytics-algorithms\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/book\/data-synthesis-for-fairness-audits-of-learning-analytics-algorithms\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip facebook\" title=\"Share on Facebook\" aria-label=\"Share on Facebook\" rel=\"noopener nofollow\"><i class=\"icon-facebook\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/x.com\/share?url=https:\/\/industry-science.com\/en\/book\/data-synthesis-for-fairness-audits-of-learning-analytics-algorithms\/\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip x\" title=\"Share on X\" aria-label=\"Share on X\" rel=\"noopener nofollow\"><i class=\"icon-x\" aria-hidden=\"true\"><\/i><\/a><a href=\"mailto:?subject=Data%20Synthesis%20for%20Fairness%20Audits%20of%20Learning%20Analytics%20Algorithms&body=Check%20this%20out%3A%20https%3A%2F%2Findustry-science.com%2Fen%2Fbook%2Fdata-synthesis-for-fairness-audits-of-learning-analytics-algorithms%2F\" class=\"icon button circle is-outline tooltip email\" title=\"Email to a Friend\" aria-label=\"Email to a Friend\" rel=\"nofollow\"><i class=\"icon-envelop\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https:\/\/industry-science.com\/en\/book\/data-synthesis-for-fairness-audits-of-learning-analytics-algorithms\/&title=Data%20Synthesis%20for%20Fairness%20Audits%20of%20Learning%20Analytics%20Algorithms\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n","protected":false},"excerpt":{"rendered":"<p>AKWI-Tagungsband zur 35. AKWI-Jahrestagung. Jahrgang, 2022, Seite S. 316\u2013320 The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be [&#8230;]\n","protected":false},"featured_media":0,"template":"","categories":[],"tags":[85196,75759,83875],"product_cat":[],"topic":[],"technology":[],"knowhow":[],"industry":[],"writer":[84890,85195],"glossary":[],"class_list":{"0":"post-111792","1":"book","2":"type-book","3":"status-publish","5":"tag-fairness-audit","6":"tag-learning-analytics","7":"tag-synthetic-data-en","8":"writer-katharina-simbeck","9":"writer-linda-fernsel","10":"product","11":"first","12":"instock","13":"downloadable","14":"virtual","15":"sold-individually","16":"taxable","17":"purchasable","18":"product-type-book"},"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Sebastian Schwarz","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"AKWI-Tagungsband zur 35. AKWI-Jahrestagung. Jahrgang, 2022, Seite S. 316\u2013320 The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/book\/111792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/book"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/book"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=111792"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=111792"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=111792"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=111792"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=111792"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=111792"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=111792"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=111792"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=111792"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=111792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}