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…

Recycling waste is essential to mitigate environmental damage caused by human activity. Environmentally responsible behaviors, shaped during early ages, are closely linked to environmental attitudes, as demonstrated by prior research. This study aims to predict middle school students' recycling behaviors using machine learning algorithms. A…

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…

This study investigates the predictive relationship between academic resilience and cognitive flexibility among students in biology lessons enhanced by design thinking. Using an embedded quasi-experimental design, we compared the effects of design thinking-enhanced lessons with conventional engage, explore, explain, elaborate, evaluate (5E)-based…

Resilient students attain high levels of academic achievement despite the presence of chronic socioeconomic disadvantage. Identifying factors that promote resilience in the domain of science is crucial to making equitable and high-quality science education accessible for all students. Rooted in the opportunity-propensity framework, this study…

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…

Background: Income inequality is theorized to impact health. However, evidence among adolescents is limited. This study examined the association between income inequality and health-related school absenteeism (HRSA) in adolescents. Methods: Participants were adolescents (n = 74,501) attending secondary schools (n = 136) that participated in the…

A multitude of research shows that the secondary education (SE) outcomes relate with university academic achievement (AA). More research is needed on SE outcomes subject-specifically. We examined the relationship between the Finnish matriculation examination (ME) results and AA, study success (grades) and study progress (credits), of educational…

PISA aims to serve as a "global yardstick" for educational success, as measured by student performance. For comparisons to be meaningful across countries or over time, PISA samples must be representative of the population of 15-year-old students in each country. Exclusions and non-response can undermine this representativeness and…

This mixed-methods research study aimed to observe the changes in relational conceptual changes and achievement in photosynthesis and cellular respiration in 15 seventh-grade students using the variation theory of learning, a framework for contextual distinctions, and supports the Common Knowledge Construction Model (CKCM) for science education.…

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,…