Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…

This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

This phenomenological study sought to examine the lived experiences of Pennsylvania school leaders in elementary or middle schools with an economically disadvantaged student population of 50% or greater and English Language Arts proficiency exceeding 60% as measured by the 2021-2022 Pennsylvania System of School Assessment. Across the Commonwealth…

Missing values that fail to be appropriately accounted for may lead to reduced statistical power, biased estimators, reduced representativeness of the sample, and incorrect interpretations and conclusions (Gorelick, 2006). The current study provided an ontological perspective of data manipulation by explaining how statistical results can…

This case study examined the initial implementation of an Early Warning Intervention System (EWIS) across three comprehensive high schools in a large suburban school district in Washington State using both quantitative and qualitative methodologies. The purpose of the study was two-fold: to determine the extent to which the initial application of…