In this study, cheating behaviors of higher education students and the changes in these behaviors due to developments in the field of artificial intelligence were examined based on the literature. The investigations show that the cheating behavior of the students is at a level that disrupts the accuracy of the decisions made about the students and academic honesty, which is one of the cornerstones of higher education. Cheating in exams, plagiarism and contract cheating are the most common of these behaviors. The way cheating behavior is displayed has also changed from past to present, depending on educational programs, measurement approaches, and developments in technology and artificial intelligence. Tremendous developments in the field of artificial intelligence in the last three years have caused students to make artificial intelligence tools an indispensable part of their cheating processes. This situation requires the development and implementation of new methods to detect and prevent cheating in higher education. Therefore, structuring new studies that address the cheating problem and its solutions in various aspects, including the technology and especially artificial intelligence dimension, is important in terms of its contribution to relevant people and institutions. It is thought that this study is important in terms of drawing attention to the issue.

This book prepares educational leaders with the knowledge needed to critically evaluate, select, and use technological tools to be effective school leaders. Authors Jones and Kennedy explore the technology tools needed to support the full range of responsibilities of a school leader, including management and administration, personnel and evaluation, security and safety, instructional leadership, organizational culture and climate, external relationships, and action research. Each chapter unpacks advantages and pitfalls of various technological tools and includes case scenarios that contextualize these ideas for readers. Chapter content is also aligned with The Professional Standards for Educational Leaders (PSEL), the National Educational Leadership Preparation Standards (NELP), and the International Society of Technology Standard in Education (ISTE) standards. This timely and important book adds to the toolbox for educators preparing to become effective and cutting-edge school leaders.

The objective of this exploratory study was to examine the interpersonal dynamics of an after-school math club for middle schoolers. Using social network analysis, 2 networks were identified and analyzed: (a) a network of friendship relationships and (b) a network of working relationships. The interconnections and correlations between friendship relationships, working relationships, and a student opinion survey were studied. We identified a core working group of students from within the network of working relations. This group acted as a central go-between for other members in the club. This core working group also expanded into the largest friendship group in the friendship network. A second group was formed from popular but aloof students who reported less impact from the club. Although there were working isolates, they were not found to be socially isolated. Students who were less popular tended to report a greater favorable impact from club participation than those who were more popular.

This chapter presents a comprehensive discussion of educational data mining and its potential for educational research. The origins of data mining and the emergence of educational data mining are discussed. The variety of data generated in education (e.g., text, speech, performance, etc.) are described and the challenges of mining these data for useful information are identified. Techniques for mining these data are discussed. Software used to mine these data are noted and issues of theory and ethics are considered. Examples from published literature are cited throughout the chapter and recommendations for educational researchers are offered.

Gate-keeping courses provide students with their first and formal exposure to a deep understanding of science. Such courses influence students' decision to pursue STEM education and continue their college experience. Our records indicate that the many STEM students perform poorly or marginally in the introductory required courses and decide to change their major to non-STEM degree programs. One way to address this is using active learning techniques. The objective of this paper is to describe our experiences with the use of few of the active learning techniques in introductory computer programming courses offered in our Computer Science Program. One of these programming courses are required of all computer science majors and other course is usually taken by engineering, technology and science majors. The findings presented in this paper may be used by interested parties involved in STEM curriculum.

Minority research and training (MRT) programs have been used across U.S. colleges and universities as a method to close the educational achievement gap and generate a highly skilled and diverse workforce. Previous studies have improved our understanding of the need to diversify the science, technology, engineering and mathematics (STEM) disciplines and the various interventions that have been developed to support these efforts. However, there is still little evidence about what strategies are most effective in promoting interest, continuation, and matriculation into STEM graduate programs among underrepresented groups. The study herein utilized a case study design with a mixed methods approach to evaluate the program impacts and outcomes of an MRT program at a research-intensive institution in the southern part of the U.S., and for program replication. This evaluation study examines the types of activities and services provided, the measurable outcomes of those activities and services, the resources used to deliver the services, the practical problems encountered, and the ways in which problems were resolved.

The importance of understanding crime in the United States assumed enhanced protrusion in the wake of the increased crime rates year by year in certain cities. Neighborhood social demographic variables have been largely used to measure their associations with crime. Other than those social factors, street lighting is a feature of urban and suburban settlement which is widely thought to be a necessary element in preventing crime. Previous research has drawn mixed conclusions about the relationship between street lighting and crime, and the effect of streetlights on neighborhood crime is not entirely definitive. To address this challenge, we examined the spatial associations between street light density, neighborhood social disorganization characteristics and crime (e.g., burglary, vehicle theft, weapons offenses, etc.) in Detroit, Michigan in 2014. Using the street lighting data from the Detroit Public Lighting Authority, crime data from the City of Detroit, supplemented with Census 2010 data, we conducted a Generalized Least Squares model of neighborhood crime in 879 census block groups to test the random effects of the spatial variables and different hours of day on crime. The results show an inverse relationship between street light density and crime rates across census block groups in Detroit and the effects of time period of a day vary according to different types of crime. These findings provided more credible evidence for researchers and policy makers to effectively optimize scarce public safety resources, such as improving street lighting in disadvantaged neighborhoods.

Many researchers have explored the relationships between the likelihood of graduating from college and demographic and pre-college factors such as gender, race/ethnicity, high school grade point average (GPA), and standardized test scores. However, additional factors such as a student’s college major, home address, or use of learning support in college have been examined to a far lesser degree. This study seeks to add these factors to an integrative persistence model in order to examine their impact on predicting college graduation in a six-year timeframe. Results indicate that students with in-state home addresses are more likely to graduate within six years than students with out-of-state home addresses, when controlling for other factors. Findings also suggest that graduation rates vary considerably for different majors and for those using learning support such as tutoring and Supplemental Instruction in college. Therefore, these additional factors become important for institutions to consider, particularly as it applies to implementing new programs, expanding programs proven effective, and/or targeting specific populations of students in order to help them persist to timely college graduation.

"Remembering Sam Stringfield (January 24, 1949 – July 31, 2016)." Journal of Education for Students Placed at Risk (JESPAR), 22(1), p. 1

This article presents results of a case study of a math circle designed for low income, minority students from an inner city middle school. The students were 6th, 7th and 8th grade African American and Hispanic males enrolled in a science, technology, engineering and mathematics focused charter school. The study focused on the impact of participation in the math circle on students and the design features of the experience that were most effective at promoting engagement and positive reactions from students. Participating students reported increases in their interests in mathematics, their confidence in their ability to tackle mathematics problems, and in their enjoyment of mathematics. Competitions and affirmation by a mathematician were key motivating factors for students. Implications for the design of math circles that promote positive mathematical identifies among marginalized populations of students are discussed.

The purpose of this publication is to provide school leaders and other educators with insight into practical uses of data and how to create school cultures conducive to effective data use. Practicing school leaders can benefit from this publication as well as teachers who use data in their classrooms to drive instruction. Another use of this book is for graduate schools that prepare K-12 school leaders.Because of accountability and the importance of data use in schools, data driven decisions and the effective use of data are critical. In A Guide to Data-Driven Leadership in Modern Schools, the use of data as aligned to educational reform is discussed. Accountability and standardized testing are vital elements of reform. The culture must be created in schools to address multi- facets of data use which is presented in Chapter 2 of the publication.The use of data should guide/inform decisions linked to both management and instruction in schools. In Chapter 3, the use of data to inform management is discussed; and the use of data to inform instruction is presented in Chapter 4. Practices of effective management and instructional leadership are obsolete without effective personnel in schools. The use of data in personnel evaluations is explored in Chapter 5.

This paper reports on the development and validation of a science inquiry skills assessment for Earth science (iSA–Earth Science) in middle schools that classroom teachers can use to assess and monitor their students' science inquiry skills. An assessment framework with six primary inquiry skills, each with three to six subskills, was developed based on a comprehensive review of the literature and following the recommendations of the National Science Education Standards (NSES; NRC, 1996) and A Framework for K–12 Science Education (FSE; NRC, 2012). The six primary skills are: (1) identify questions for scientific investigations, (2) design scientific investigations, (3) use tools and techniques to gather data, (4) analyze and describe data, (5) explain results and draw conclusions, and (6) recognize alternative explanations and predictions. The assessment development and validation process followed guidelines from the Standards for Educational and Psychological Testing (American Educational Research Association [AERA], American Psychological Association [APA], and National Council of Measurement in Education [NCME], 1999). Psychometric analysis using both classical test statistics and Item Response Theory showed the instrument was internally consistent, reliable, and did not function differentially for male and female students. Implications for science education practice and research are discussed.