Temperament in the Classroom

Variance in academic performance that persists when situational variables are held constant suggests that whether students fail or thrive depends not only on circumstance, but also on relatively stable individual differences in how children respond to circumstance. More academically talented children generally outperform their less able peers, but much less is known about how traits unrelated to general intelligence influence academic outcomes. This paper addresses several related questions: What insights can be gleaned from historical interest in the role of temperament in the classroom? What does recent empirical research say about the specific dimensions of temperament most important to successful academic performance? In particular, which aspects of temperament most strongly influence school readiness, academic achievement, and educational attainment? What factors mediate and moderate associations between temperament and academic outcomes? What progress has been made in deliberately cultivating aspects of temperament that matter most to success in school? And, finally, for researchers keenly interested in better understanding how and why temperament influences academic success, in which direction does future progress lie?

Trajectories of university adjustment in the United Kingdom: Emotion management and emotional self-efficacy protect against initial poor adjustment

Little is known about individual differences in the pattern of university adjustment. This study explored longitudinal associations between emotional self-efficacy, emotion management, university adjustment, and academic achievement in a sample of first year undergraduates in the United Kingdom (N=331). Students completed measures of adjustment to university at three points during their first year at university. Latent Growth Mixture Modeling identified four trajectories of adjustment: (1) low, stable adjustment, (2) medium, stable adjustment, (3) high, stable adjustment, and (4) low, increasing adjustment. Membership of the low, stable adjustment group was predicted by low emotional self-efficacy and low emotion management scores, measured at entry into university. This group also had increased odds of poor academic achievement, even when grade at entry to university was controlled. Students who increased in adjustment had high levels of emotion management and emotional self-efficacy, which helped adaptation. These findings have implications for intervention

Educational anomaly analytics : features, methods, and challenges

Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field. Copyright © 2022 Guo, Bai, Tian, Firmin and Xia

Using Big Data to determine potential dropouts in higher education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics

Retraction: using Big Data to determine potential dropouts in higher education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics

The influences of course effort and outside activities on grades in a college course

The influences of course effort and outside (family, job, social) activities on grades earned in a college course were examined for 230 urban college students. Multiple measurements of hours of work, social and family activities, and course effort were collected over a semester. Path modeling revealed that cumulative GPA and course effort had significant and independent predictive paths with grades. Outside activities did not directly influence course grade. Job activities, however, negatively influenced course grade indirectly through reduced course effort and mediated the influence GPA exerted on course grade. Thus, work demands lessened course effort and lessened GPA-indexed potential for course success. Cumulative GPA positively influenced effort, and effort mediated part of the relation between cumulative GPA and grades

Temperament in the Classroom

Some students fare better than others, even when researchers control for family background, school curriculum, and teacher quality. Variance in academic performance that persists when situational variables are held constant suggests that whether students fail or thrive depends on not only circumstance but also relatively stable individual differences in how children respond to circumstance. More academically talented children, for instance, generally outperform their less able peers. Indeed, general intelligence, defined as the ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought (Neisser et a!., 1996, p. 77), has a monotonic, positive relationship with academic performance, even at the extreme right-tail of the population (Gottfredson, 2004; Lubinski, 2009). Much less is known about how traits unrelated to general intelligence influence academic outcomes. This chapter addresses several related questions: What insights can be gleaned from historical interest in the role of temperament in the classroom? What does recent empirical research say about the specific dimensions of temperament most important to successful academic performance? In particular, which aspects of temperament most strongly influence school readiness, academic achievement, and educational attainment? What factors mediate and moderate associations between temperament and academic outcomes? What progress has been made in deliberately cultivating aspects of temperament that matter most to success in school? And, finally, for researchers keenly interested in better understanding how and why temperament influences academic success, in which direction does future progress lie

Student Loan Default: Do Characteristics of Four-Year Institutions Contribute to the Puzzle?

College student debt and loan default are growing concerns in the United States. For each U.S. institution, the federal government is now reporting a cohort default rate, which is the percent of students who defaulted on their loan, averaged over a three-year period. Previous studies have amply shown that student characteristics are strongly associated with educational debt and one’s ability to repay student loans; however, few studies have deeply examined the relationship between institutional characteristics and student loan default. This study examined characteristics of 1,399 four-year notfor-profit U.S. institutions and found significant differences in the 2010 federal student loan default rate by some important institutional variables, including admissions yield, geographic region, percent of minority students, institution control (private versus public), endowment, and expenditures for student services. Findings related to institutional characteristics can illuminate our understanding of the student loan default puzzle, and have implications for student success, academic policy, and resource allocation decisions

An Analysis of Long-Term Financial Feasibility of Higher Education

This project explores the long-term financial feasibility of higher education. With rising costs of higher education and so many choices surrounding a degree such as degree type, sector of institution one attends, student loans one takes out, and field of study, it can be hard to discover which path will be most profitable long-term. This project analyzes data from the National Center of Education Statistics to see if there are existing relationships between these variables that contribute to different experiences in higher education and financial outcomes, specifically relating to future income and student loan payments. To do this I use various statistical tools and models such as multiple linear regression, tests for correlation, Kmeans clustering, and ANOVA testing. While most of these tests showed little or no relationship or significance, through clustering I found that those who get both an associate’s and a bachelor’s in the same field, make on average significantly more than those who get an associate’s and bachelor’s degrees in different fields. I also start the creation of a summary statistics interface with the intent to display data in a way that those with minimal scientific background could understand in the hopes that this project will continue to spark conversations around the inaccessibility of data surrounding higher education and the realistic outcomes that different paths through higher education will provide