Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for…

Globally, statistical analyses have found a range of variables that predict the odds of first-year students failing to progress at their Higher Education Institution (HEI). Some of these studies have included students from a range of disciplines. Yet despite the rise in the number of criminology students in HEIs in the UK, little statistical…

The Torrance Test of Creative Thinking (TTCT) is the most widely used norm-referenced creativity test used in gifted identification. Although commonly used for identifying talent, little is known about how creativity tests, like the TTCT-Figural, contribute to the probability of being identified as gifted especially with underrepresented…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

Students placed in special education programs for emotional and behavioral disorders with emotional disturbance (ED) identification have academic outcomes that lag both students in regular and special education. This issue is especially important for youth attending urban schools. Although prior research has examined students identified as ED,…

This dissertation is comprised of three studies using restricted data from the ECLS-K:1998 to address the questions who is placed in special education? and what happens after they are placed? Though these questions have been extensively studied, existing research has largely ignored the intersection of longitudinal developmental pathways and…

Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders…

Aim: The aim of this study was to identify subgroups of children born moderately preterm (MPT) and term with distinctive levels and patterns of functioning, and the perinatal and demographic factors that predict subgroup membership. Method: A total of 378 children aged 7 years, 248 MPT (138 males, 110 females; gestational age 32-36 wks) and a…

Value-added models and growth-based accountability aim to evaluate school's performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new…

This presentation focuses on the quasi-experimental methods used to select comparison schools for an evaluation of a federal investing in innovation (i3) validation grant. The Northeast Tennessee College and Career Ready Consortium (NETCO) consists of 29 high schools participating in a five-year program to expand students' access to rigorous…

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…