INTRODUCTION
In past decades, exclusionary practices such as suspension have garnered much attention from the media, but no distinct solution has proven to reduce the number of suspensions (Rafa, 2018). According to the U. S. State Department of Education, millions of students in grades K-12 received an out-of-school suspension. More than 2.7 million K – 12 public school students received one or more out-of-school suspensions in the 2015 – 2016 academic year nationwide (U.S. Department of Education, Office of Civil Rights, 2018). The number of suspensions is staggering for most states. For example, South Carolina reported a total of 694,402 violent or drug-related offenses resulting in OSS or expulsion for the 2018-2019 school year (South Carolina Department of Education, 2019).
School districts are desperately looking for solutions to this massive problem. In California, student suspensions were approximately 381,845 for the 2016-2017 school year (Losen & Martin, 2018). Despite the fact exclusionary practices have failed to curb student misbehavior, the method is still widely used today. The purpose of this study was to investigate a high school in-school program to determine any disparities among race gender, age, and grade point averages of suspended students. This chapter lays out the theoretical and empirical frameworks for examining schools and their relationships to student outcomes in the form of outof-school suspensions. The following section details the scholarship on students and out-of-school suspensions followed by an exploration of schools and suspensions. Finally, I outline the analyses of the three ensuing chapters and how they expand on existing scholarship.
Research Questions
Below are the questions that guided this study to investigate a high school ISS program:
- What differences were observed in student GPAs when comparing the ISS and OSS populations?
- What differences were observed in the collected data when controlling for age?
- What differences were observed in the collected data when controlling for gender?
Sample Solution
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