IU School of Social Work Collection

Permanent URI for this collection

https://hdl.handle.net/1805/4065

Browse

Browsing IU School of Social Work Collection by Subject "absenteeism"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A Change in the Frame: From Absenteeism to Attendance
    (Frontiers, 2020) Gentle-Genitty, Carolyn; Taylor, James; Renguette, Corinne; School of Social Work
    School attendance is important for student long-term academic and career success. However, in the U.S., our current practice often disenfranchises more at-risk students than it helps. Students slated for suspension and expulsion are often recipients of these practices. This manuscript offers a recommended change in how we frame student absenteeism and attendance using attendance markers and conceptual information by identifying the discrepancies, proposing options, and recommending a new way to actively leverage attendance data (not absenteeism data) for proactive student support. Particular attention is paid to how excused and unexcused absences and in-school suspensions are treated. An emerging pivot program, the Evaluation and Support Program, engages students while they receive school services, community support, and complete consequences is discussed as a possible, promising intervention.
  • Thumbnail Image
    Item
    Improving school attendance by enhancing communication among stakeholders: establishment of the International Network for School Attendance (INSA)
    (Springer, 2019) Heyne, David; Gentle-Genitty, Carolyn; Landell, Malin Gren; Melvin, Glenn; Chu, Brian; Gallé-Tessonneau, Marie; Askeland, Kristin Gärtner; Gonzálvez, Carolina; Havik, Trude; Ingul, Jo Magne; Johnsen, Daniel Bach; Keppens, Gil; Knollmann, Martin; Lyon, Aaron R.; Maeda, Naoki; Reissner, Volker; Sauter, Floor; Silverman, Wendy K.; Thastum, Mikael; Tonge, Bruce J.; Kearney, Christoper A.; School of Social Work
  • Thumbnail Image
    Item
    Revealing underlying factors of absenteeism: A machine learning approach
    (Frontiers, 2022-12) Bowen, Francis; Gentle-Genitty, Carolyn; Siegler, Janaina; Jackson, Marlin; School of Social Work
    Introduction: The 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. Methods: This 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. Results: From 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. Discussion: The 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.