School districts increasingly gauge school quality with surveys that ask about school climate and student engagement. We use data from New York City's middle and high schools to compare the long-run predictive validity of surveys with that of conventional test score value-added models (VAMs). Our analysis leverages the New York school match, which…

Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…

Bullying, particularly among teenagers and young adults, is one of the most important issues facing school communities. At its very heart, this issue speaks to a troubling form of deviant behavior. When students engage in bullying behaviors, the effects are felt far beyond that of the direct victim. As such, it is important to investigate the…

Algorithms may be better at prediction than humans in a variety of contexts, but they are not perfect. A deeper understanding of the ways in which educators use and question algorithmic advice within their professional domain is needed. Educators are a particularly unique professional group, in comparison with the other groups studied in the…

Choosing a field of study (study major) is challenging for prospective students. However, little research has examined factors measured prior to enrollment to predict motivation and well-being in a specific study major. Based on literature on affective forecasting and person-environment fit, prospective students' well-being forecast could be such…

Organizational commitment and knowledge management are both crucial factors in the growth of an organization. The quantitative, correlational-predictive study was to examine if and to what extent the constituents of KM together predicted organizational commitment for high school employees in USA. Based on the premise that the relationship of…

Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school…

This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses,…

The objectives of this research were 1) to study the problems of stress and depression among Grade 12 students; 2) to investigate the machine learning technique in analyzing and predicting stress, depression, and academic performance among Grade 12 students; and 3) to evaluate the stress and depression prediction platform. Students from schools in…

As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational…

This article focuses on the possible relationship between predictions, developed during the resolution of geometrical tasks within a Paper-and-Pencil Environment (PPE), and subsequent explorations within a Dynamic Geometry Environment (DGE). Building on Fischbein's Theory of Figural Concepts, and to gain insight into the transition of predictions…

Care leavers, one of the most vulnerable groups in society, are underrepresented in higher education (HE). This study follows 16 entire birth cohorts of alumni of youth villages in Israel (born 1982-1997, N = 44,164) and is based on national administrative data. Using Propensity Score Matching we created a double sized comparison group from the…

Self-directed learning (SDL) is a basic individual ability in modern society. It is of great value to explore SDL and its relationship to learners' online learning effectiveness. This study explored the relationships among online learners' personality traits (neuroticism and extraversion), SDL (SDL-approach and SDL-attitude), and perceived online…

Direct certification has been described by policymakers and academics as a tool which may replace National School Lunch Program (NSLP) eligibility data (Douglas Geverdt, National Center for Education Statistics, personal communication, August 28, 2023). It suggests a policy future in which we change the metric of how we identify disadvantage. On…

Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We…