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Direct linkKearney, Christopher A.; Childs, Joshua – Improving Schools, 2023
School attendance and absenteeism are critical targets of educational policies and practices that often depend heavily on aggregated attendance/absenteeism data. School attendance/absenteeism data in aggregated form, in addition to having suspect quality and utility, minimizes individual student variation, distorts detailed and multilevel…
Descriptors: Data Analysis, Attendance, Educational Policy, Causal Models
Jialun Pan; Zhanzhan Zhao; Dongkun Han – IEEE Transactions on Learning Technologies, 2025
Properly predicting students' academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to…
Descriptors: Prediction, Academic Achievement, At Risk Students, Artificial Intelligence
Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students
