Students with or at-risk of High Incidence Disabilities (HID) experience negative short and long-term outcomes. To intervene, many schools have elected to implement evidence-based practices within Multi-Tiered Systems of Support (MTSS), such as Response to Intervention (RTI). MTSS target the academic and behavioral progress of students deemed 'at…

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…

Higher education institutions have prioritized supporting undecided students with their major and career decisions for decades. This study used a U.S. public research-focused university's large-scale institutional data set and undecided student's retention and graduation rate predictors to demonstrate how to couple student and institutional data…

Accountability has pushed principals to use data to drive and inform decisions in schools to positively impact student achievement. Research has shown that principals are the second most important impact on student achievement, second only to teachers. Principals who can lead change in schools based on data driven response have a positive impact…

College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address…

In tiered instructional systems (Response to Intervention [RTI]/Multitier System of Supports [MTSS]) that rely on ongoing assessment of students at risk of experiencing academic difficulties, the ability to make informed decisions using student data is critical for student learning. Prior research has demonstrated that, on average, teachers have…

This study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor's degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features' selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin,…

Objective/Research Question: Community college graduation rates are typically quite low, and developmental mathematics enrollment and coursetaking patterns may constrain academic outcomes. To identify ways in which community college graduation rates may be improved, decision trees were utilized to examine the STEM coursetaking patterns of N =…

Considering the large volume of PISA data, it is expected that data mining will often be assisted in making PISA data more meaningful. Studies show that different dimensions of ICT may reveal different relationships for mathematics achievement. The purpose of this article is to evaluate the success of the decision tree classification algorithms in…

Every day, students collect, organize, and analyze data to make decisions. In this data-driven world, people need to assess how much trust they can place in summary statistics. The results of every survey and the safety of every drug that undergoes a clinical trial depend on the correct application of appropriate statistics. Recognizing the…

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating…

This study evaluated the components of the theory of planned behavior as related to teachers' intentions to use data-informed decision-making (DIDM) when evaluating both student progress and their own instructional practices. Analysis was also conducted to determine if there is any difference in data use between special education/intervention…

Data-driven decision making is a critical leadership skill. This study describes how leadership at a four-year university used extant data to improve student outcomes. The University identified the high rate of first-time-in-college (FTIC) student withdrawal/failure in initial algebra courses as having a detrimental effect on other student success…

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…