Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…

Few studies have investigated eyewitnesses' ability to predict their later lineup performance, known as "predecision confidence." We applied calibration analysis in two experiments comparing predecision confidence (immediately after encoding but prior to a lineup) to postdecision confidence (immediately after a lineup) to determine which…

The aim of this study is to examine the relationships between pre-service teachers' critical thinking skills and their self-efficacy for argumentation. The participants of the research consisted of 858 pre-service teachers (447 female, 411 male) studying in education faculties at five different state universities in Turkey. In this study, the…

Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and…

Background: When developing walking programmes for improving health in adults with Down syndrome (DS), physical activity professionals are in need of an equation for predicting energy expenditure. We therefore developed and cross-validated an equation for predicting the rate of oxygen uptake (VO[subscript 2]; an index of energy expenditure) for…

A historic period of state divestment in public higher education, exacerbated by the "Great Recession" and attendant financial repercussions, has significantly altered public higher education financing. The most significant impact has been cost shift from the state to students via increasing tuition rates. These changes threaten student…

This longitudinal, quasi-experimental field study investigated affective forecasting as a moderator of positive intergroup contact effects among adolescents. We also examined a novel mediating mechanism that underlies this effect, namely accuracy of perceived outgroup willingness for intergroup contact. Three annual waves of survey data were used…

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

Purpose: The purpose of this study is to investigate how epidemiological and clinical factors collectively predict whether a preschooler who is stuttering will persist or recover and to provide guidance on how clinicians can use these factors to evaluate a child's risk for stuttering persistence. Method: We collected epidemiological and clinical…

Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches.…

A renewed interest in cloud computing adoption has occurred in academic and industry settings because emerging technologies have strong links to cloud computing and Big Data technology. Big Data technology is driving cloud computing adoption in large business organizations. For cloud computing adoption to increase, cloud computing must transition…

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…

Curriculum Associates' i-Ready is an adaptive diagnostic and individualized instructional tool that has been used in M-DCPS in the last few years. In addition, Curriculum Associates provides the District with results of their predictive model, which uses the students' outcomes on the Fall and Winter i-Ready diagnostic testing as well as the…

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction…