Research over the monitoring of at-risk youth. Behavioral interventions have been the primary focus of this literature past two decades has focused on one aspect of dropout prevention: early identification an however, there is renewed emphasis placed on attendance and academic risk factors under ESSA. Recognizing a need to promote early…

Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…

With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which…

Surveys of teacher preparation program (TPP) completers have become one widely used measure for program accountability and improvement, yet there is little evidence as to whether perceptions of preparation experiences predict the workforce outcomes of teachers. In the present study, we use statewide completer survey data from North Carolina to…

School dropout is a growing concern across Latin America because of its negative social and economic consequences. Identifying who is likely to drop out, and therefore could be targeted for interventions, is a well-studied prediction problem in countries with strong administrative data. In this paper, we use new data in Guatemala and Honduras to…

Measures of student disadvantage--or risk--are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new…

Many schools and districts have considerable discretion when hiring teachers, yet little is known about how that discretion should be used. Using data from a new teacher screening system in the Los Angeles Unified School District (LAUSD), we find that performance during screening, and especially performance on specific screening assessments, is…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

The purpose of this research is to first estimate the item and ability parameters and the standard error values related to those parameters obtained from Unidimensional Item Response Theory (UIRT), bifactor (BIF) and Testlet Response Theory models (TRT) in the tests including testlets, when the number of testlets, number of independent items, and…

Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead…

The field of educational data mining has been using the Knowledge Tracing model, which only look at the correctness of student first response, for tracking student knowledge. Recently, lots of other features are studied to extend the Knowledge Tracing model to better model student knowledge. The goal of this paper is to analyze whether or not the…

This study aimed at providing explanation and prediction of principals' inclusive education intentions and practices under the framework of the Theory of Planned Behaviour (TPB). A sample of 209 principals from Hong Kong schools was surveyed using five scales that were developed to assess the five components of TPB: attitude, subjective norm,…

When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…

Little is known about the influence of language background in problem-based learning (PBL) tutorial groups on group processes and students' academic achievement. This study investigated the relationship between language background, secondary school score, tutorial group processes, and students' academic achievement in PBL. A validated tutorial…

This study investigates the effect of correctional instructions when detecting lies about relational aggression. Based on models from the field of social psychology, we predict that correctional instruction will lead to a less pronounced lie bias and to more accurate lie detection. Seventy-five teachers received videotapes of students' true denial…