[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119627-en":3,"doc-seo-119627-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119627,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Time management and absenteeism: studying the students through machine learning","Absenteeism in higher education generates institutional, economic, social, and individual consequences. This study examines whether students’ personal time management can explain absenteeism rates, using machine learning combined with explainable artificial intelligence. A two-level analysis is built from a global and an individual perspective, identifying how specific behaviors relate to class attendance. Repeating a course shows the most negative effect, while submitting assignments before deadlines shows the most positive effect. Academic career type, living location, and hobbies also influence absenteeism patterns.","HEAd  \n10th International Conference on Higher Education Advances (HEAd'24) Universitat Politcnica de Valncia, Valncia, 2024 DOI: [https://doi.org/10.4995/HEAd24.2024.17343](https://doi.org/10.4995/HEAd24.2024.17343)  \nTime management and absenteeism: studying the students through machine learning  \nSantiago Porras1 , Athénaïs Sauvée2, Julio César Puche1, Silvia Casado1, Paula Antón1, Joaquín Antonio Pacheco1  \n1Department of Applied Economics, University of Burgos, Spain, 2Department of Private Law, University of Burgos, Spain.  \nHow to cite: Porras, S. ; Sauvée, A. ; Puche, J. C. ; Casado, S. ; Antón, P. ; Pacheco, J. A. 2024. Time management and absenteeism: studying the students through machine learning. In: 10th International Conference on Higher Education Advances (HEAd’24) . Valencia, 18-21 June 2024. [https://doi.org/10.4995/HEAd24.2024.17343](https://doi.org/10.4995/HEAd24.2024.17343)  \nAbstract  \nAbsenteeism in higher education is a problem that may involve institutional, economic, social, and individual consequences. The present work aims to analyse whether the way students manage their personal time could be an explanation for absenteeism rates. Authors used machine learning based methodology, combined with explainable artificial intelligence methods. This allowed them to design a two-levels analysis, it is to say from a global, and an individual perspective. Factors such as repeating a course have the most negative impact over class attendance. On the contrary, being able to submit an assignment before the deadline has the most positive impact over class attendance. The kind of academic career, the place of living or the hobbies has also influence over the absenteeism.  \nKeywords: Absenteeism; Higher education; Support vector machine, Explainable artificial intelligence, Shapley additive explanation, Time management.  \n1. Introduction  \nAbsent is always in the wrong, he who is. Absenteeism is often linked to poor academic performance and high drop-out rates. This has consequences in terms of wasted time and economic resources, as well as the need for continuous review of education policies in both schools and higher education institutions.(Abadzi, 2012; Keppens, 2023; Miller, 2023) .  \nTo address absenteeism, it is crucial to identify its underlying factors. Sahin et al. (2016) identify five socio-economic sources: familial factors, administrative and teacher-related factors, factors related to the organization of the school, the student's own factors, and external environmental factors such as transportation. At the individual level, the main causes of absence are common health problems, early class schedules, stress, and depression (Şahín 2023) . Other factors that  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 673  \nmay contribute to absenteeism include the student's organization, teaching and learning methodologies, course characteristics, and external factors (Triado-Ivern et al. 2020) . Ramchander (2017) explains that homework load is a factor that can contribute to absenteeism. Students may be absent to prepare for tests, assignments, or group work, particularly on the days leading up to a test or on the day of the test itself.  \nIt should be noted that the above is not an exhaustive list of the factors contributing to absenteeism. Other causes of absenteeism require further investigation. This paper analyses one factor related to students: their time management and how the latter affects their absenteeism. Other variables will also be considered within the study. To do so, we begin with the previous findings of Porras et al. (2023), who examined time management among first-year students of“Administración y dirección de empresas ” (ADE) and “Finanzas y contabilidad' (FICO) degrees at a Spanish university. The study identified four explanatory factors. The current study includes additional academic and personal variables of the students,","cbCairI0QYxU7PCC","https://ap.wps.com/l/cbCairI0QYxU7PCC","pdf",327148,1,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Dataset\n## Regression analysis","[{\"question\":\"What problem does the paper address in higher education?\",\"answer\":\"The paper focuses on absenteeism in higher education and its potential consequences.\"},{\"question\":\"How does the study link time management to absenteeism?\",\"answer\":\"It applies machine learning with explainable AI to analyze whether students’ personal time management helps explain absenteeism rates and class attendance.\"},{\"question\":\"Which factors show the strongest relationships with class attendance?\",\"answer\":\"Repeating a course most negatively affects attendance, while submitting assignments before deadlines has the most positive impact. The academic career track, living place, and hobbies also play a role.\"}]","Time management and absenteeism: studying the students through machine learning | PDF",1785725370,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"time-management-and-absenteeism-studying-the-students-through-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/time-management-and-absenteeism-studying-the-students-through-machine-learning/119627/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address in higher education?","Question",{"text":74,"@type":75},"The paper focuses on absenteeism in higher education and its potential consequences.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the study link time management to absenteeism?",{"text":79,"@type":75},"It applies machine learning with explainable AI to analyze whether students’ personal time management helps explain absenteeism rates and class attendance.",{"name":81,"@type":72,"acceptedAnswer":82},"Which factors show the strongest relationships with class attendance?",{"text":83,"@type":75},"Repeating a course most negatively affects attendance, while submitting assignments before deadlines has the most positive impact. 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