Revealing underlying factors of absenteeism: A machine learning approach
IntroductionThe basis of support is understanding. In machine learning, understanding happens through assimilated knowledge and is centered on six pillars: big data, data volume, value, variety, velocity, and veracity. This study analyzes school attendance problems (SAP), which encompasses its legal...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.958748/full |
| _version_ | 1828098001126555648 |
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| author | Francis Bowen Carolyn Gentle-Genitty Janaina Siegler Marlin Jackson |
| author_facet | Francis Bowen Carolyn Gentle-Genitty Janaina Siegler Marlin Jackson |
| author_sort | Francis Bowen |
| collection | DOAJ |
| description | IntroductionThe basis of support is understanding. In machine learning, understanding happens through assimilated knowledge and is centered on six pillars: big data, data volume, value, variety, velocity, and veracity. This study analyzes school attendance problems (SAP), which encompasses its legal statutes, school codes, students’ attendance behaviors, and interventions in a school environment. The support pillars include attention to the physical classroom, school climate, and personal underlying factors impeding engagement, from which socio-emotional factors are often the primary drivers.MethodsThis study asked the following research question: What can we learn about specific underlying factors of absenteeism using machine learning approaches? Data were retrieved from one school system available through the proprietary Building Dreams (BD) platform, owned by the Fight for Life Foundation (FFLF), whose mission is to support youth in underserved communities. The BD platform, licensed to K-12 schools, collects student-level data reported by educators on core values associated with in-class participation (a reported—negative or positive—behavior relative to the core values) based on Social–Emotional Learning (SEL) principles. We used a multi-phased approach leveraging several machine learning techniques (clustering, qualitative analysis, classification, and refinement of supervised and unsupervised learning). Unsupervised technique was employed to explore strong boundaries separating students using unlabeled data.ResultsFrom over 20,000 recorded behaviors, we were able to train a classifier with 90.2% accuracy and uncovered a major underlying factor directly affecting absenteeism: the importance of peer relationships. This is an important finding and provides data-driven support for the fundamental idea that peer relationships are a critical factor affecting absenteeism.DiscussionThe reported results provide a clear evidence that implementing socio-emotional learning components within a curriculum can improve absenteeism by targeting a root cause. Such knowledge can drive impactful policy and programming changes necessary for supporting the youth in communities overwhelmed with adversities. |
| first_indexed | 2024-04-11T07:54:36Z |
| format | Article |
| id | doaj.art-33b53ba82631422e92ffc5b09c855f89 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-11T07:54:36Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-33b53ba82631422e92ffc5b09c855f892022-12-22T04:35:58ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-12-011310.3389/fpsyg.2022.958748958748Revealing underlying factors of absenteeism: A machine learning approachFrancis Bowen0Carolyn Gentle-Genitty1Janaina Siegler2Marlin Jackson3Data Analytics and Business Intelligence Lacy School of Business, Butler University, Indianapolis, IN, United StatesSchool of Social Work, Indiana University Bloomington, Bloomington, IN, United StatesData Analytics and Business Intelligence Lacy School of Business, Butler University, Indianapolis, IN, United StatesFight for Life Foundation, Indianapolis, IN, United StatesIntroductionThe basis of support is understanding. In machine learning, understanding happens through assimilated knowledge and is centered on six pillars: big data, data volume, value, variety, velocity, and veracity. This study analyzes school attendance problems (SAP), which encompasses its legal statutes, school codes, students’ attendance behaviors, and interventions in a school environment. The support pillars include attention to the physical classroom, school climate, and personal underlying factors impeding engagement, from which socio-emotional factors are often the primary drivers.MethodsThis study asked the following research question: What can we learn about specific underlying factors of absenteeism using machine learning approaches? Data were retrieved from one school system available through the proprietary Building Dreams (BD) platform, owned by the Fight for Life Foundation (FFLF), whose mission is to support youth in underserved communities. The BD platform, licensed to K-12 schools, collects student-level data reported by educators on core values associated with in-class participation (a reported—negative or positive—behavior relative to the core values) based on Social–Emotional Learning (SEL) principles. We used a multi-phased approach leveraging several machine learning techniques (clustering, qualitative analysis, classification, and refinement of supervised and unsupervised learning). Unsupervised technique was employed to explore strong boundaries separating students using unlabeled data.ResultsFrom over 20,000 recorded behaviors, we were able to train a classifier with 90.2% accuracy and uncovered a major underlying factor directly affecting absenteeism: the importance of peer relationships. This is an important finding and provides data-driven support for the fundamental idea that peer relationships are a critical factor affecting absenteeism.DiscussionThe reported results provide a clear evidence that implementing socio-emotional learning components within a curriculum can improve absenteeism by targeting a root cause. Such knowledge can drive impactful policy and programming changes necessary for supporting the youth in communities overwhelmed with adversities.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.958748/fullabsenteeismschool attendance problemsmachine learningsocio-emotional learningclassificationmulti-tiered systems of support |
| spellingShingle | Francis Bowen Carolyn Gentle-Genitty Janaina Siegler Marlin Jackson Revealing underlying factors of absenteeism: A machine learning approach Frontiers in Psychology absenteeism school attendance problems machine learning socio-emotional learning classification multi-tiered systems of support |
| title | Revealing underlying factors of absenteeism: A machine learning approach |
| title_full | Revealing underlying factors of absenteeism: A machine learning approach |
| title_fullStr | Revealing underlying factors of absenteeism: A machine learning approach |
| title_full_unstemmed | Revealing underlying factors of absenteeism: A machine learning approach |
| title_short | Revealing underlying factors of absenteeism: A machine learning approach |
| title_sort | revealing underlying factors of absenteeism a machine learning approach |
| topic | absenteeism school attendance problems machine learning socio-emotional learning classification multi-tiered systems of support |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.958748/full |
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