Intersections between Education and Juvenile Justice Research

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Assignment: Building on your earlier project and homework submissions, the goals of this project are to (1) review and synthesize existing work in your chosen topic area and evaluate questions addressed in this work, (2) examine visualizations that have been used to communicate insights on your topic, (3) develop a conceptual model (DAG) that represents your group's current understanding of the topic, (4) communicate key insights to multiple audiences, and (5) presents your topic and questions as a potential avenue for further research. Below is a list of guiding questions and parameters for each task. (1) Problem and questions (individual) - 5pts.: Write a 2-4 pages (double-spaced) brief that synthesizes the problem you are addressing, existing resources/ literature, and the questions you will be addressing (potential guiding questions below). Explain how you will be addressing the problem and answering your questions. What is the broad problem that you will be addressing? What did you find in the existing literature? (Ex. This might be gaps in existing national surveys) What questions are you answering? How do your questions relate to the problem and how could their answers advance solutions? Are your questions predictive or inferential in nature? Are your questions appropriately framed and well described? Explain how you will be addressing the problem and answering your questions. Note: your writing does not need to address all of these question and should not necessarily be constrained to only these topics. We are looking for a cohesive narrative rather than answers to discrete questions. (2) Visualization or Survey (individual) - 4 pts.: 2 pages (double-spaced) including your visualizations OR your survey tool including your questions with the possible responses. The writing for this task should be a general consideration of visualizations you did generate, or will generate based on survey responses, Explain why you chose to either use particular types of visualizations OR asked specific questions on your survey. Note: please reproduce the visualizations/ survey questions you discuss in the documents instead of simply providing links. (3) Conceptual Models (group) - 4 pts.: Using the individual DAGs submitted by each group member for Homework #6 as a starting point, develop a DAG which represents your group's current understanding of the topic. Develop a single document with individual DAGs for each team member (reproduced from Homework #4) and one or more versions of your group DAG. Each entry in the document should include an image and the Daggity-generated code if you used Daggity. Focus on building a DAG that represents your informed intuitions about the topic/problem at the core of your project. It is not necessary to have a documented empirical grounding for every relationship represented. (4) Communicating Insights (Individual) - 4 pts.: Using Chapter 10 of the Art of Data Science text as reference, either communicate your findings (for existing data) or describe the way that you will analyze the data in order to communicate your findings. This can draw from HW 5 and can explain the way that you will address your questions. (5) Presentation (group) - 8 pts: All group members contribute Introduces your topic broadly and makes a case for the questions you want to examine. Consider the presentation a pitch to decision makers about moving your projects forward. Include a complete list of resources you relied upon at the end of your presentation. We are not concerned with citation format, but all resources should be easily accessible Dosbol is the best 2 Educational Equity Problem Statement Equitable education is a major concern in the United States. As the government provides public education for the country's children, the concern over equal access to quality education is immense. The main question of concern in this evaluation is whether the government has achieved equal access to education for every child across the United States . Research has concluded that the government has done tremendously well in providing education for all children, but equal access to quality education varies from state to state and even neighbourhood to neighbourhood. While there has been considerable research addressing this problem, this review will discuss three articles that answer three different questions related to education parity . Relation with the main theme First, Ainscow discusses the challenges governments face in achieving educational equity (Ainscow, 2020). Ainscow demonstrated that there were tremendous difficulties affecting government initiatives that hindered equitable access. Gamson et al. furthered this debate and attempted to evaluate the various legislation aimed at achieving educational parity (Gamson et al., 2015). This evaluation helped discuss the impact of the Elementary and Secondary Education act. Lastly, Hanson evaluated the impact of budgetary constraints on equal access to education. This evaluation helps us understand the impact of budgetary inequalities on attempts to promote equal access to education. States spend differently on education based on available resources, resulting in unequal distribution of quality schools, highly qualified teachers, and associated learning materials. This inequality contributes significantly to the problem of unequal access to education. Questions answered Dosbol is the best 3 Ainsworth sought to identify the challenges that governments encountered to education parity. The findings showed that racial and ethnic discrimination were the greatest threats to parity. In addition, the disparities of law and curriculum from one state to the other also contributed to educational inequalities. Gamson et al. sought to answer whether we have adequate laws to facilitate equal access to education across the United States. The study findings showed that the Elementary and Secondary Education Act played a major role in closing the achievement gap (Gamson et., 2015). The Act guaranteed every American child free basic education from elementary school right through secondary school. Hanson evaluated various scenarios that demonstrated how financial inequalities had contributed to equal educational access. The study's findings showed a great difference between how much states spent on every learner from one state to the other. Relation with the Problem Statement The main problem being evaluated through these questions is the problem of inequal access to education. Ainscow's review discusses the challenges that governments face when attempting to ensure equal access to education. Through this review, we understand the challenge of achieving education equality from the perspective of the government. The review of laws that affect education equality helps us appreciate other law reforms that facilitate equal access to education for all. The Elementary and Secondary Education Act was developed to guarantee every child equal access to basic education. This understanding can help learners evaluate the progress that successive governments have made to be where we are as a country. Lastly, the review by Hanson evaluates the impact of budgetary inequalities in achieving education equality. Benefits of the solutions Dosbol is the best 4 Answering each of these questions would help advance educational equality. The challenge of discrimination affects any attempts to enhance equality in education access. Dealing with racism and bias would help marginalized communities access education, thus enhancing equality in education access. These laws would also fast-track all other interventions that have been established to enhance education equality. The evaluation of laws affecting education equality shed light on the importance of legal frameworks in education. When the law guarantees a child the right to education, denial of service cannot be an option and would only be granted if the circumstances fit provisions made in the law. Lastly, solutions to budgetary inequalities would help allocate appropriate resources to promote equal growth and equal access to quality education across the world. Bridging the gap between schools in wealthy neighborhoods ad schools in shanty neighborhoods would result in equal access to educations for children despite their neighborhood. Predictive/Inferential The question on challenges that governments face in the attempt to is predictive. It anticipates the availability of factors that hinder equal access to education. A predictive question leads to an almost predetermined conclusion that matches the question asked in Ainsworth's research. The question of law reforms in ensuring education equality by Gamson et al. is an inferential question. It concludes that many laws affect education in general and hinder or encourage equal access to education. An inferential question points out a conclusion that makes it easy to guess the facts around the question. The question of budgetary inequalities by Hanson is a predictive one. It predicts that when there are budgetary inequalities, there are bound to be inequalities in any given scenario. These questions were properly framed and described the problem of unequal access to education effectively. Visualization Dosbol is the best 5 Visualizations were major communication cues in the three research reviews we discussed. Visualizations refer to the imagery set to describe the severity of a scenario. They help focus attention on the main points in any discussion, which is important to get the message passed across. Ainsworth's review painted the picture of challenges such as discrimination and how it hinders equal access to education. The Grimm picture of how a black child is likely to miss out on education compared to a white child visualizes the extent of the challenge. This visualization targets education policymakers to highlight the plight of minorities and formulate policies to protect them. Evaluating the challenge of legal reforms necessary to promote equal access to education presents a vivid picture of the struggle for equality. Reading through the history of the Elementary and Secondary Education Act of 1945 paints the picture of a nation in search of equality in education. This visualization targets lawmakers to rise to the occasion and utilize their strong position to influence laws that guarantee equal access to education for all children. The review on the impact of budgetary inequalities visualizes the effects of economic disparities on attempts to promote equality in education. This evaluation targets budgeting officials to ensure adequate resources to every educational institution to allow equal access to quality education. Communication Insights When communicating the importance of enhancing education equity, I would frame my communication for two different audiences. To the school managers and school heads, I would encourage annual gatherings to set plans to encourage equal access and evaluate progress periodically. I would sensitize my classmates and colleagues to social media on the importance of equality in education. Dosbol is the best 6 References Ainscow, M. (2020). Inclusion and equity in education: Making sense of global challenges. Prospects, 49(3), 123-134. Doi: 10.1007/s11125-020-09506-w Gamson, D. A., McDermott, K. A., & Reed, D. S. (2015). The Elementary and Secondary Education Act at fifty: Aspirations, effects, and limitations. RSF: The Russell Sage Foundation Journal of the Social Sciences, 1(3), 1-29. Doi: 10.7758/rsf.2015.1.3.01 Hanson, Melanie. (2021) "U.S. Public Education Spending Statistics" EducationData.org, https://educationdata.org/public-education-spending-statistics Equitable Educational Access Question: Does the government provide equal access to education to all children? Theme: Education Equity This theme engages with education, not only in the current information age, but generally. People can realize the good and the bad and engage their high thinking faculties when they access quality education. Education also helps people understand the importance of working hard and being intelligent, growing, and developing as individuals. Education also promotes creativity and innovation. A country gets a better society through quality education that highlights the importance of respecting laws, sets regulations, rights, and freedom, and improves socialization with people from other races and tribes. Thus, equitable education access presents a theme that engages crucial societal needs. Governments should provide equitable education of high quality to grow economically. Questions a) What are the challenges experienced in the provision of equitable educational access? b) Does any supporting act exist, and are they followed by the administration to guarantee success for all students? c) Are there sufficient funds allocated towards attaining equal access to education? These questions can help address the existing disparities in the education sector and the stakeholder’s challenges in implementing the necessary policies to guarantee equitable educational access. The questions can point to the underlying issues in the education sector and why equity might be lacking. Problem For the education system to succeed, the government must ensure that it provides equitable educational access. It can only achieve this by ensuring enough resources are allocated and that the right measures are put in place to ensure students from all backgrounds have equal access to education and ensure that quality education is provided to all students. However, ensuring equity in education is a challenge. Firstly, while other countries are making tremendous achievements in education, the United States of America (USA) must close the existing gaps that inhibit success. The schools in low-income communities do not receive sufficient resources to help their soutcomes match those in high-income neighborhoods. A case of low attendance is also experienced in low-income areas. In addition, there are high rates of expulsion, dropping out of school, and lack of access to solid curricula and teachers. Such disparities present a problem in the achievement of equity in education access. Student 1: Challenges Experienced in Provision of Equitable Educational Access In the USA, everyone works hard to live a more fulfilling life. It is a country that draws its strength from the common core values as a land of equal opportunity for all. However, economic mobility is increasingly compared to other countries. One of the solutions to this issue is access to equaitable world-class education to ensure all children reach their full potential and live a good life. The main challenge is that many students in disadvantaged communities do not have access to vital components of quality education. These challenges range from free quality preschool, unequal standards, and teaching that engages the studentsin leadership skills . In addition, there is unequal support, resource allocation, and failure to afford high-quality education among disadvantaged groups. Despite the existing challenges, the government has been trying to advance equity levels in education; however, more needs to be done to address disparities. Student 2: Acts that support Equitable Education Access to Guarantee Success Education in the USA has undergone various reforms. In the early days before the industrial revolutions, education depended on gender, race, location, and social classes. Basic numeracy and literacy were widely available to people in the Northern areas; however, the same was limited in the rural southern regions. Since then, many changes have witnessed the establishment of common schools, parochial schools, among others. Higher education was introduced in the nineteenth century. Several changes have also been put in place as education advancements were made. For instance, in 1965, the Elementary and Secondary Education Act (ESEA) was amended (U.S). Other acts also improved education for marginalized communities, such as individuals with disabilities and higher education acts. This evidence shows that these education acts support equitable educational access. Thus, the implementation of such actions and policies could be one of the contributing factors to achieving equitable educational access. Student 3: Funds Allocation Towards the Goal of Achieving Equal Access to Education The relationship that exists between resources allocated to education and the achieved outcomes is critical. The directed funds should be used appropriately for the desired results to be completed. Both local and federal governments allocate funds to education during budgeting. The spending on public education in the USA falls short of global benchmarks established by international organizations (Hanson, 2021). the public funds allocated to the education sector is 11.6% which is below the 15% international standards. Compared to other countries as shown in the table below, USA spending is low. Table 1 Country Elementary Schools Secondary Schools Luxembourg $12,892 $20,413 Korea $11,047 $12,202 France $7,395 $11,747 Slovenia $8,542 $8,290 Spain $7,320 $9,020 Mexico $2,874 $3,129 Colombia $3,178 $2,817 Indonesia $1,514 $1,435 Note: This table shows other countries’ spending compared to the USA, whose budget is $12,624 per pupil on average. Adopted from Hanson, Melanie. (2021) “U.S. Public Education Spending Statistics” EducationData.org, https://educationdata.org/public-education-spending-statistics Evidence shows that higher spending on education leads to better outcomes in schools. This success is also experienced among low-income students. This way th states can create more equitable systems of financing, and the student’s accomplishment rises. Budgets should support the expansion of educational opportunities for all students in three main areas: increased access to data that drives informed decision-making, diversity in schools, and high-quality learning. For instance, the 2017 budget had local support that created variety in schools. Figure 1 Public K-12 Spending per Student Note: This map shows average spending per student based on the 2021 budget The issue of funds allocation in education is a meaningful discussion. The supreme court of New Jersey gave a detailed description of school funding systems that ensure equal access to primary educational services. Advocates should focus on equal educational opportunities since this is the driving force of the goal of equitability when it comes to financing in the education sector (Martin et al., 2018). Thus, the priority should be equal access to high-quality education opportunities, which depends on the budgeting and allocating funds to schools and students. References Hanson, Melanie. (2021) “U.S. Public Education Spending Statistics” EducationData.org, https://educationdata.org/public-education-spending-statistics Martin. C, Boser. U, Benner. M, & Baffour. P. (2018). A Quality Approach to School Funding. https://educationdata.org/public-education-spending-statistics U.S. Department of Education. (n.d.). Equity of Opportunity. https://www.ed.gov/equity U.S. Department of Education. (n.d.). Laws & Guidance. https://www2.ed.gov/policy/landing.jhtml?src=ft Aliev 1 Dosbol Aliev Professor Ariel Ludwig HON 3350 October 23, 2021 The Directed Acyclic Graph (DAG) There is a direct relationship between cancer and smoking. Many studies have proved that smoking is one of the causes of cancer complications. In this case, the explanatory variable (exposure variable) is smoking, with cancer being the dependent variable, also called the outcome variable. The development of smoking is believed to be physical activity, and thus smoking will lead to cancer through the physical act of smoking. The variable physical activities act as the mediator to developing cancer. Excessive consumption of alcohol can also lead to cancer. Also, a combination of alcohol and smoking causes cancer. This variable, alcohol or excessive consumption of alcohol, is thus a confounder variable. Lastly, once an individual develops cancer, it will lead to early or premature death (lowering life expectancy); therefore, premature death is a collider with smoking and cancer. Figure: DAG DISTRICT Abington Heights SD Abington SD Albert Gallatin Area SD Aliquippa SD Allegheny Valley SD Allegheny-Clarion Valley SD Allentown City SD Altoona Area SD Ambridge Area SD Annville-Cleona SD Antietam SD Apollo-Ridge SD Armstrong SD Athens Area SD Austin Area SD Avella Area SD Avon Grove SD Avonworth SD Bald Eagle Area SD Baldwin-Whitehall SD Bangor Area SD Beaver Area SD Bedford Area SD Belle Vernon Area SD Bellefonte Area SD Bellwood-Antis SD Bensalem Township SD Benton Area SD Bentworth SD Berlin Brothersvalley SD Bermudian Springs SD Berwick Area SD Bethel Park SD Bethlehem Area SD Bethlehem-Center SD Big Beaver Falls Area SD Big Spring SD Blackhawk SD Blacklick Valley SD Blairsville-Saltsburg SD Bloomsburg Area SD Blue Mountain SD Enrollment 3393 7434 3575 1189 1025 774 17560 7910 2737 1506 1045 1327 5450 2243 222 600 5263 1517 1834 4197 3232 1977 1864 2662 2870 1282 6266 724 1201 865 2031 3062 4621 14427 1267 1711 2780 2459 688 1784 1619 2895 # of Schools 6 9 9 2 3 2 21 13 5 4 3 3 12 7 2 2 4 3 5 5 5 4 4 5 6 3 9 2 3 3 3 6 8 22 3 4 5 5 2 5 5 5 Miconduct Incidents Incidents Involving Reported to Local Law PDE Offenders Enforcement 16 14 0 33 32 30 121 141 91 34 44 4 19 15 14 38 29 0 3618 2252 343 205 213 130 224 183 35 17 16 17 70 68 8 43 39 16 94 79 8 100 76 53 13 12 0 56 40 5 78 92 28 9 8 0 28 25 6 62 56 3 109 96 83 25 24 13 51 49 6 80 71 22 63 60 49 45 51 7 205 197 34 35 37 6 44 43 2 7 12 4 28 28 5 132 112 4 169 123 0 867 731 104 103 96 20 41 53 14 39 43 33 136 118 0 15 21 6 25 29 10 66 66 19 15 14 14 Blue Ridge SD Boyertown Area SD Bradford Area SD Brandywine Heights Area SD Brentwood Borough SD Bristol Borough SD Bristol Township SD Brockway Area SD Brookville Area SD Brownsville Area SD Burgettstown Area SD Burrell SD Butler Area SD California Area SD Cambria Heights SD Cameron County SD Camp Hill SD Canon-McMillan SD Canton Area SD Carbondale Area SD Carlisle Area SD Carlynton SD Carmichaels Area SD Catasauqua Area SD Centennial SD Central Bucks SD Central Cambria SD Central Columbia SD Central Dauphin SD Central Fulton SD Central Greene SD Central Valley SD Central York SD Chambersburg Area SD Charleroi SD Chartiers Valley SD Chartiers-Houston SD Cheltenham Township SD Chester-Upland SD Chestnut Ridge SD Chichester SD Clairton City SD Clarion Area SD Clarion-Limestone Area SD Claysburg-Kimmel SD Clearfield Area SD Coatesville Area SD 1074 7144 2554 1661 1278 1278 6154 1015 1561 1797 1324 1862 7473 949 1456 688 1262 4958 1004 1580 4852 1426 1076 1526 5728 20081 1772 1922 10831 969 1955 2382 5638 8667 1627 3447 1167 4458 3944 1645 3389 750 824 924 858 2404 6953 3 10 4 4 4 3 12 2 4 5 2 4 14 4 3 2 4 11 2 2 10 3 2 3 7 23 4 3 19 3 3 4 7 16 3 4 2 7 9 4 6 2 2 2 2 6 10 42 84 173 17 19 23 280 24 22 142 19 20 138 10 7 34 15 51 17 78 78 24 18 27 177 284 32 69 260 12 116 60 22 255 81 89 22 145 281 39 42 37 52 121 6 79 40 40 90 127 18 25 35 272 24 24 95 14 25 132 12 5 26 13 53 20 71 74 34 24 36 171 247 29 53 283 10 94 55 34 252 73 91 26 148 286 38 56 33 39 88 6 74 36 12 72 78 4 1 0 27 14 12 123 0 10 60 3 3 5 6 22 0 0 35 3 0 24 50 79 11 0 216 0 3 20 19 114 1 0 12 57 7 10 33 5 1 13 0 32 21 Cocalico SD Colonial SD Columbia Borough SD Commodore Perry SD Conemaugh Township Area SD Conemaugh Valley SD Conestoga Valley SD Conewago Valley SD Conneaut SD Connellsville Area SD Conrad Weiser Area SD Cornell SD Cornwall-Lebanon SD Corry Area SD Coudersport Area SD Council Rock SD Cranberry Area SD Crawford Central SD Crestwood SD Cumberland Valley SD Curwensville Area SD Dallas SD Dallastown Area SD Daniel Boone Area SD Danville Area SD Deer Lakes SD Delaware Valley SD Derry Area SD Derry Township SD Donegal SD Dover Area SD Downingtown Area SD Dubois Area SD Dunmore SD Duquesne City SD East Allegheny SD East Lycoming SD East Penn SD East Pennsboro Area SD East Stroudsburg Area SD Eastern Lancaster County SD Eastern Lebanon County SD Eastern York SD Easton Area SD Elizabeth Forward SD Elizabethtown Area SD Elk Lake SD 3221 4698 1325 515 975 899 4249 3981 2360 4695 2827 657 4669 2224 848 11643 1172 3890 3011 7849 1126 2772 6036 3845 2417 1951 5160 2195 3601 2864 3638 11779 3996 1567 409 1831 1662 8033 2752 7666 3091 2457 2602 9016 2415 3963 1271 5 7 3 2 2 3 6 5 6 11 4 2 6 6 2 15 3 8 4 10 3 4 8 6 4 4 7 3 5 6 6 15 10 3 1 3 4 10 4 10 5 5 5 10 6 7 2 25 23 14 20 11 31 57 62 144 90 129 0 21 57 11 52 35 56 44 121 27 36 66 91 105 80 72 33 51 26 157 146 121 45 18 183 31 181 50 161 143 103 21 96 53 111 21 25 33 14 13 14 37 50 64 136 92 108 0 20 47 9 59 30 51 39 141 21 31 70 93 92 71 75 37 48 35 155 142 104 34 17 146 33 183 52 195 101 75 21 85 58 98 25 25 23 11 2 11 8 56 36 11 55 28 0 21 33 2 28 17 1 18 60 17 3 53 15 12 3 45 22 40 26 38 17 70 11 9 8 1 74 22 145 39 8 12 78 10 25 6 Ellwood City Area SD Ephrata Area SD Erie City SD Everett Area SD Exeter Township SD Fairfield Area SD Fairview SD Fannett-Metal SD Farrell Area SD Ferndale Area SD Fleetwood Area SD Forbes Road SD Forest Area SD Forest City Regional SD Forest Hills SD Fort Cherry SD Fort LeBoeuf SD Fox Chapel Area SD Franklin Area SD Franklin Regional SD Frazier SD Freedom Area SD Freeport Area SD Galeton Area SD Garnet Valley SD Gateway SD General McLane SD Gettysburg Area SD Girard SD Glendale SD Governor Mifflin SD Great Valley SD Greater Johnstown SD Greater Latrobe SD Greater Nanticoke Area SD Greencastle-Antrim SD Greensburg Salem SD Greenville Area SD Greenwood SD Grove City Area SD Halifax Area SD Hamburg Area SD Hampton Township SD Hanover Area SD Hanover Public SD Harbor Creek SD Harmony Area SD 1918 4148 12324 1415 4257 1165 1585 542 856 774 2681 458 535 835 1986 1133 2144 4349 2016 3710 1223 1538 1956 358 4794 3780 2154 3023 1922 793 4217 4029 3138 4162 2209 3003 2901 1417 796 2590 1122 2390 3090 2018 1651 2005 361 4 6 23 5 6 3 3 3 2 2 5 2 4 2 3 2 5 6 8 5 4 5 5 1 5 7 4 5 3 2 6 6 4 5 5 4 5 3 3 6 4 4 5 4 5 5 3 130 91 4259 68 99 36 96 63 20 27 26 9 20 13 37 1 55 28 142 39 11 75 16 24 19 71 11 69 50 8 68 27 22 143 45 21 178 25 26 102 14 21 36 20 123 39 28 115 88 1926 54 104 33 66 43 20 37 31 15 20 12 37 2 44 22 114 40 10 75 20 19 24 69 11 73 43 12 75 25 25 125 44 26 120 33 23 86 15 21 40 19 92 38 22 21 36 90 4 28 19 0 5 8 18 24 9 2 0 13 0 2 16 38 24 0 13 8 0 19 29 5 22 5 0 33 19 18 12 16 7 57 16 11 14 8 4 6 6 31 15 3 Harrisburg City SD Hatboro-Horsham SD Haverford Township SD Hazleton Area SD Hempfield SD Hempfield Area SD Hermitage SD Highlands SD Hollidaysburg Area SD Homer-Center SD Hopewell Area SD Huntingdon Area SD Indiana Area SD Interboro SD Iroquois SD Jamestown Area SD Jeannette City SD Jefferson-Morgan SD Jenkintown SD Jersey Shore Area SD Jim Thorpe Area SD Johnsonburg Area SD Juniata County SD Juniata Valley SD Kane Area SD Karns City Area SD Kennett Consolidated SD Keystone SD Keystone Central SD Keystone Oaks SD Kiski Area SD Kutztown Area SD Lackawanna Trail SD Lakeland SD Lake-Lehman SD Lakeview SD Lampeter-Strasburg SD Lancaster SD Laurel Highlands SD Laurel SD Lebanon SD Leechburg Area SD Lehighton Area SD Lewisburg Area SD Ligonier Valley SD Line Mountain SD Littlestown Area SD 6691 4962 5635 10337 6947 6081 2086 2672 3457 893 2382 2063 2730 3581 1244 559 1172 832 626 2654 2106 616 3017 754 1204 1581 4276 1116 4460 2079 3869 1491 1135 1623 2019 1228 3085 10851 3263 1316 4598 783 2347 1901 1681 1270 2062 11 8 7 10 10 11 5 6 6 2 5 4 6 6 2 2 3 2 2 6 3 2 11 2 3 4 6 2 12 5 9 5 2 3 4 3 5 20 6 2 7 2 6 4 4 4 4 299 67 23 224 78 23 81 130 47 34 40 49 114 84 21 27 29 10 0 106 60 8 95 19 20 22 134 3 251 32 8 34 9 54 49 27 25 670 85 10 184 10 25 39 53 115 42 342 88 28 280 74 37 88 110 45 27 34 41 92 85 21 23 33 14 0 97 61 9 83 22 18 19 114 4 236 31 13 41 17 43 40 22 32 650 82 10 144 7 24 32 46 86 40 54 37 19 131 68 8 7 2 34 2 40 19 6 14 18 0 4 0 0 40 20 1 16 1 5 7 27 0 84 30 0 12 5 33 0 2 13 127 0 7 4 4 23 12 15 2 29 Lower Dauphin SD Lower Merion SD Lower Moreland Township SD Loyalsock Township SD Mahanoy Area SD Manheim Central SD Manheim Township SD Marion Center Area SD Marple Newtown SD Mars Area SD McGuffey SD McKeesport Area SD Mechanicsburg Area SD Mercer Area SD Methacton SD Meyersdale Area SD Mid Valley SD Middletown Area SD Midd-West SD Midland Borough SD Mifflin County SD Mifflinburg Area SD Millcreek Township SD Millersburg Area SD Millville Area SD Milton Area SD Minersville Area SD Mohawk Area SD Monessen City SD Moniteau SD Montgomery Area SD Montour SD Montoursville Area SD Montrose Area SD Moon Area SD Morrisville Borough SD Moshannon Valley SD Mount Carmel Area SD Mount Pleasant Area SD Mount Union Area SD Mountain View SD Mt Lebanon SD Muhlenberg SD Muncy SD Nazareth Area SD Neshaminy SD Neshannock Township SD 3786 7342 2141 1471 1038 2956 5926 1411 3347 3161 1908 3763 3655 1303 5161 906 1732 2297 2202 357 5416 2153 7350 843 686 2211 1237 1499 932 1455 904 2896 1940 1552 3679 898 922 1553 2117 1515 1146 5278 3571 1001 4609 8833 1318 8 10 3 3 3 5 9 3 6 5 4 6 8 3 8 3 3 5 6 1 10 6 13 3 2 5 3 2 3 2 3 5 4 3 7 3 2 3 6 5 2 10 4 2 6 12 2 37 165 7 9 106 87 75 9 38 18 75 4 86 20 106 20 130 74 82 1 194 29 128 15 13 163 61 6 40 88 16 110 18 27 35 12 53 132 13 37 28 33 95 13 60 121 12 36 173 10 10 75 82 96 10 35 20 79 4 75 19 72 21 91 75 67 1 187 31 152 13 12 136 50 10 53 69 15 120 10 30 36 12 52 110 18 36 24 31 96 12 55 132 10 35 28 5 9 0 5 66 3 2 13 33 1 77 2 62 3 39 34 20 1 26 3 55 11 5 0 5 1 12 12 7 2 18 3 7 0 3 33 2 8 8 11 20 2 40 32 2 New Brighton Area SD New Castle Area SD New Hope-Solebury SD New Kensington-Arnold SD Newport SD Norristown Area SD North Allegheny SD North Clarion County SD North East SD North Hills SD North Penn SD North Pocono SD North Schuylkill SD North Star SD Northampton Area SD Northeast Bradford SD Northeastern York SD Northern Bedford County SD Northern Cambria SD Northern Lebanon SD Northern Lehigh SD Northern Potter SD Northern Tioga SD Northern York County SD Northgate SD Northwest Area SD Northwestern SD Northwestern Lehigh SD Norwin SD Octorara Area SD Oil City Area SD Old Forge SD Oley Valley SD Oswayo Valley SD Otto-Eldred SD Owen J Roberts SD Oxford Area SD Palisades SD Palmerton Area SD Palmyra Area SD Panther Valley SD Parkland SD Pen Argyl Area SD Penn Cambria SD Penn Hills SD Penn Manor SD Penncrest SD 1650 3260 1604 2238 1147 6972 8199 598 1666 4232 12649 3113 1896 1143 5543 807 3786 1087 1222 2354 1793 580 2088 3159 1207 1151 1572 2278 5199 2583 2136 939 1837 509 711 5036 3791 1810 1843 3263 1713 9285 1774 1741 3987 5133 3403 3 7 4 6 3 11 12 2 4 6 17 5 2 3 6 2 8 2 3 6 4 2 5 6 3 3 4 4 7 4 6 2 3 3 2 7 6 5 5 6 3 11 3 5 5 10 6 49 91 32 64 61 1086 93 7 75 184 244 296 107 31 68 28 50 16 12 28 54 5 87 33 42 42 52 30 45 67 115 31 53 3 15 122 76 19 26 64 24 133 54 54 242 93 42 41 102 28 67 50 816 85 7 58 153 253 200 91 34 67 38 55 15 13 28 50 5 75 39 48 42 42 30 44 56 84 25 47 3 14 128 79 19 31 62 26 142 36 49 312 88 42 5 0 5 3 13 35 15 1 27 9 79 22 37 13 66 6 45 9 0 16 12 1 19 17 24 2 32 4 6 28 26 3 5 3 0 26 23 16 18 20 13 68 23 11 42 44 21 Penn-Delco SD Pennridge SD Penns Manor Area SD Penns Valley Area SD Pennsbury SD Penn-Trafford SD Pequea Valley SD Perkiomen Valley SD Peters Township SD Philadelphia City SD Philipsburg-Osceola Area SD Phoenixville Area SD Pine Grove Area SD Pine-Richland SD Pittsburgh SD Pittston Area SD Pleasant Valley SD Plum Borough SD Pocono Mountain SD Port Allegany SD Portage Area SD Pottsgrove SD Pottstown SD Pottsville Area SD Punxsutawney Area SD Purchase Line SD Quaker Valley SD Quakertown Community SD Radnor Township SD Reading SD Red Lion Area SD Redbank Valley SD Reynolds SD Richland SD Ridgway Area SD Ridley SD Ringgold SD Riverside Beaver County SD Riverside SD Riverview SD Rochester Area SD Rockwood Area SD Rose Tree Media SD Saint Clair Area SD Saint Marys Area SD Salisbury Township SD Salisbury-Elk Lick SD 3377 7374 938 1460 10574 4179 1695 5814 4401 154262 1831 3321 1588 4624 26653 3294 5636 4037 10176 932 918 3301 3061 2928 2258 1014 1943 5250 3606 18060 5586 1192 1234 1594 969 5805 3036 1579 1549 1035 879 771 3754 581 2232 1608 276 6 11 2 4 15 8 4 7 5 251 5 6 3 6 64 5 6 7 12 2 2 5 7 3 8 3 4 10 5 24 9 3 2 2 3 9 4 3 3 3 2 2 6 1 5 4 2 101 18 21 108 531 53 15 130 65 8062 24 36 35 31 4084 74 44 86 37 16 22 203 379 236 47 32 14 99 61 1056 117 21 36 120 20 43 205 32 12 68 58 19 71 22 21 46 16 79 16 15 90 375 49 23 113 47 8894 24 34 28 42 3477 81 52 90 47 15 19 146 320 182 42 26 12 91 58 1187 146 20 23 84 18 43 198 32 12 46 54 17 67 19 17 41 12 3 17 16 10 0 25 8 15 21 3414 9 15 1 18 1 7 43 10 36 15 2 70 83 33 19 2 11 31 11 179 54 8 24 6 19 31 50 2 8 0 9 15 6 0 4 41 0 Saucon Valley SD Sayre Area SD Schuylkill Haven Area SD Schuylkill Valley SD Scranton SD Selinsgrove Area SD Seneca Valley SD Shade-Central City SD Shaler Area SD Shamokin Area SD Shanksville-Stonycreek SD Sharon City SD Sharpsville Area SD Shenandoah Valley SD Shenango Area SD Shikellamy SD Shippensburg Area SD Slippery Rock Area SD Smethport Area SD Solanco SD Somerset Area SD Souderton Area SD South Allegheny SD South Butler County SD South Eastern SD South Fayette Township SD South Middleton SD South Park SD South Side Area SD South Western SD South Williamsport Area SD Southeast Delco SD Southeastern Greene SD Southern Columbia Area SD Southern Fulton SD Southern Huntingdon County SD Southern Lehigh SD Southern Tioga SD Southern York County SD Southmoreland SD Spring Cove SD Spring Grove Area SD Springfield SD Springfield Township SD Spring-Ford Area SD State College Area SD Steel Valley SD 2348 1133 1322 1933 9798 2739 7257 556 4797 2447 402 2108 1326 1158 1212 2957 3439 2196 941 3680 2280 6658 1566 2634 2910 2548 2152 2079 1191 4058 1315 3989 623 1428 847 1240 3086 1975 3172 1973 1819 3790 3754 2188 7818 6848 1800 3 2 3 3 17 4 9 2 8 3 3 5 3 2 2 6 6 5 2 7 4 10 3 4 6 3 4 3 3 6 3 6 2 3 2 5 6 6 5 4 4 6 5 4 12 11 5 66 38 57 19 213 46 76 18 138 193 30 39 19 245 20 63 129 37 34 103 56 160 38 36 34 13 15 90 74 53 13 797 31 37 4 239 13 120 54 50 70 64 176 51 83 320 99 48 29 47 18 181 48 72 16 152 132 21 50 24 150 18 60 127 33 27 103 54 141 37 43 32 12 19 90 63 45 12 673 27 36 4 132 14 91 51 41 62 71 130 58 76 214 95 16 3 16 14 101 9 23 1 40 3 0 29 5 4 4 19 24 10 5 19 23 13 0 2 31 5 3 17 5 12 2 33 0 7 0 5 13 23 12 2 45 43 17 12 18 16 6 Steelton-Highspire SD Sto-Rox SD Stroudsburg Area SD Sullivan County SD Susquehanna Community SD Susquehanna Township SD Susquenita SD Tamaqua Area SD Titusville Area SD Towanda Area SD Tredyffrin-Easttown SD Trinity Area SD Tri-Valley SD Troy Area SD Tulpehocken Area SD Tunkhannock Area SD Turkeyfoot Valley Area SD Tuscarora SD Tussey Mountain SD Twin Valley SD Tyrone Area SD Union Area SD Union City Area SD Union SD Uniontown Area SD Unionville-Chadds Ford SD United SD Upper Adams SD Upper Darby SD Upper Dauphin Area SD Upper Dublin SD Upper Merion Area SD Upper Moreland Township SD Upper Perkiomen SD Upper Saint Clair SD Valley Grove SD Valley View SD Wallenpaupack Area SD Wallingford-Swarthmore SD Warren County SD Warrior Run SD Warwick SD Washington SD Wattsburg Area SD Wayne Highlands SD Waynesboro Area SD Weatherly Area SD 1316 1383 5452 641 859 2906 1811 2104 2095 1645 6457 3313 843 1510 1497 2736 390 2584 1105 3393 1912 817 1212 592 2912 4101 1153 1662 12216 1249 4257 3963 3069 3200 4138 955 2554 3461 3390 4764 1664 4388 1540 1536 2930 4328 675 2 3 9 2 2 4 3 5 6 3 8 6 3 3 3 6 2 6 5 5 3 3 3 3 9 6 2 5 14 3 6 6 4 4 6 2 4 5 6 12 4 6 3 3 6 6 3 88 103 279 12 5 87 35 70 63 101 94 167 26 42 57 147 82 30 36 117 81 6 85 20 234 35 11 23 298 38 62 59 57 83 102 28 61 121 98 62 35 184 61 62 93 90 45 93 108 210 13 5 105 25 69 66 82 78 127 21 36 44 124 58 31 30 99 59 6 66 19 173 33 11 22 424 40 65 61 45 75 82 25 49 106 93 55 31 154 72 59 72 93 37 50 4 56 6 4 52 9 33 11 2 37 14 25 20 33 13 7 9 0 8 74 4 25 2 15 23 4 19 82 9 29 11 9 8 52 23 0 35 10 58 10 57 12 24 14 47 8 Wellsboro Area SD West Allegheny SD West Branch Area SD West Chester Area SD West Greene SD West Jefferson Hills SD West Middlesex Area SD West Mifflin Area SD West Perry SD West Shore SD West York Area SD Western Beaver County SD Western Wayne SD Westmont Hilltop SD Whitehall-Coplay SD Wilkes-Barre Area SD Wilkinsburg Borough SD William Penn SD Williams Valley SD Williamsburg Community SD Williamsport Area SD Wilmington Area SD Wilson SD Wilson Area SD Windber Area SD Wissahickon SD Woodland Hills SD Wyalusing Area SD Wyoming Area SD Wyoming Valley West SD Wyomissing Area SD York City SD York Suburban SD Yough SD 1543 3271 1147 11827 739 2790 1070 2990 2600 7962 3113 743 2219 1657 4215 7044 1100 5289 1039 519 5436 1339 5875 2179 1249 4428 4048 1403 2482 4785 1860 5196 2990 2196 4 5 2 16 4 5 4 6 5 16 6 2 4 3 5 9 5 11 2 2 9 4 10 5 3 7 8 2 5 9 3 10 6 5 20 21 96 215 84 36 16 207 47 136 72 12 54 0 117 101 992 461 214 16 75 28 134 62 39 77 154 50 45 104 78 1389 50 13 17 21 70 223 57 37 14 170 44 148 78 13 51 0 107 116 367 458 138 15 74 26 124 43 26 81 215 61 41 112 77 954 53 14 10 11 1 112 1 8 4 30 22 82 19 1 20 0 92 101 5 105 7 3 57 12 52 12 6 11 92 5 7 49 18 71 8 6 Total Arrests 0 2 82 2 0 0 21 28 16 13 1 0 0 3 0 1 1 0 2 0 29 1 1 4 4 0 1 1 0 0 1 0 0 87 19 1 5 0 0 1 5 5 Assignments to Alternative Education 0 0 9 3 0 0 12 19 10 0 7 0 1 3 0 0 8 0 1 3 0 1 3 0 10 0 2 1 0 0 4 0 0 1 1 0 4 0 0 0 3 3 11 51 23 0 0 0 2 8 3 123 0 1 0 3 2 1 2 0 0 0 3 0 0 14 22 27 0 3 67 0 0 0 14 92 0 0 0 5 1 7 19 0 1 0 0 0 15 4 0 0 0 0 2 0 0 3 16 0 1 0 2 0 1 0 1 0 0 11 0 6 5 1 3 2 0 31 0 0 0 0 2 1 0 0 9 0 5 1 1 1 2 0 3 0 19 0 0 0 1 0 44 1 0 41 0 0 7 3 0 2 0 0 0 28 0 0 6 3 3 0 28 18 13 21 30 10 58 1 3 0 4 14 9 36 1 1 0 45 0 22 2 3 3 0 0 0 3 2 1 7 14 6 0 4 0 0 1 0 0 6 1 0 0 0 3 0 1 15 2 2 7 7 4 0 1 9 3 0 0 3 24 1 0 2 6 2 0 4 19 15 4 4 2 0 0 0 3 1 1 6 0 0 0 0 0 3 12 2 0 0 0 0 0 18 0 1 0 0 19 0 1 0 0 4 7 1 1 0 2 0 1 0 2 5 0 5 1 33 2 3 3 1 1 4 12 1 3 1 0 8 0 0 0 0 0 0 3 0 0 2 10 0 0 4 0 7 4 5 2 2 2 0 4 0 1 0 0 0 2 1 0 1 44 2 12 103 39 0 0 0 10 0 0 15 4 0 0 0 0 0 0 19 1 0 0 0 2 0 1 0 25 22 0 9 0 2 0 0 3 54 0 0 16 0 22 2 0 3 25 0 3 0 32 11 3 0 7 4 0 1 2 3 0 3 0 0 0 0 3 2 1 0 1 0 0 1 0 2 1 0 2 1 6 1 0 4 3 0 0 1 0 2 2 0 1 14 15 24 3 0 0 0 40 0 0 0 0 1 27 0 11 0 1 26 11 0 5 0 3 0 5 0 2 0 10 1 6 0 2 0 0 0 0 15 0 0 4 0 0 1 17 8 1 10 6 1 0 0 0 0 1 2 0 0 0 0 0 3 0 9 4 0 0 1 1 9 3 5 0 0 1 0 7 5 0 0 2 1 4 0 3 1 0 4 0 4 1 3 0 1 0 0 0 0 3 33 0 0 29 0 27 6 24 8 51 1 22 9 0 8 2 0 13 5 19 0 3 0 0 0 0 0 0 0 0 17 0 0 12 9 0 55 5 3 0 14 11 0 0 0 0 2 0 0 3 4 4 6 0 5 0 18 4 6 6 3 5 1 0 0 3 6 1 1 2 0 2 20 0 0 0 0 0 0 1 0 6 8 6 3 3 0 51 7 0 3 11 0 0 14 0 0 0 1495 1 3 0 0 0 0 16 1 7 4 0 46 34 28 13 0 0 1 0 9 45 1 7 0 0 3 50 0 0 0 0 12 0 0 5 25 0 0 7 1 0 0 10 0 0 0 1292 6 0 0 4 363 0 1 14 2 1 0 2 25 1 6 0 1 6 0 1 3 2 1 0 0 4 9 0 0 0 0 3 0 0 3 3 0 3 0 0 1 88 2 17 1 0 2 0 4 4 0 0 5 14 6 2 16 3 1 0 1 16 1 0 0 0 0 1 12 0 0 0 0 4 1 0 0 0 32 2 5 7 0 1 2 0 0 1 0 2 7 2 0 0 0 1 2 1 1 3 8 3 0 1 9 0 0 0 1 2 0 0 4 0 0 3 0 0 1 0 0 0 0 0 2 6 2 0 1 0 0 18 0 49 3 0 19 0 0 0 0 1 4 1 1 29 0 2 4 0 0 52 0 0 1 1 2 0 0 60 0 2 1 2 0 0 15 0 1 2 10 0 4 4 2 2 16 5 1 1 14 1 1 5 4 3 5 0 1 5 3 1 0 17 3 9 0 0 1 2 4 0 0 2 0 0 10 0 2 0 0 0 5 3 0 1 1 13 0 0 3 15 5 6 0 0 1 0 9 0 2 2 0 4 25 4 0 7 0 61 101 3 25 1 0 38 0 2 4 0 0 4 4 3 33 0 0 1 0 0 0 1 5 0 4 0 4 0 1 15 0 0 0 0 1 0 8 1 4 5 0 9 0 0 0 0 5 0 2 1 0 0 0 DISTRICT Abington Heights SD Abington SD Albert Gallatin Area SD Aliquippa SD Allegheny Valley SD Allegheny-Clarion Valley SD Allentown City SD Altoona Area SD Ambridge Area SD Annville-Cleona SD Antietam SD Apollo-Ridge SD Armstrong SD Athens Area SD Austin Area SD Avella Area SD Avon Grove SD Avonworth SD Bald Eagle Area SD Baldwin-Whitehall SD Bangor Area SD Beaver Area SD Bedford Area SD Belle Vernon Area SD Bellefonte Area SD Bellwood-Antis SD Bensalem Township SD Benton Area SD Bentworth SD Berlin Brothersvalley SD Bermudian Springs SD Berwick Area SD Bethel Park SD Bethlehem Area SD Bethlehem-Center SD Big Beaver Falls Area SD Big Spring SD Blackhawk SD Blacklick Valley SD Blairsville-Saltsburg SD Bloomsburg Area SD Blue Mountain SD Blue Ridge SD Boyertown Area SD School Security Officers 0 0 6 7 0 0 52 1 0 0 0 1 0 0 0 0 1 0 0 0 12 0 0 5 0 0 0 0 0 0 0 0 5 3 0 0 0 0 0 0 0 0 0 0 School Police Officers 0 0 1 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 1 School Resource Officers 1 2 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 2 0 0 0 0 0 1 8 0 0 0 0 0 0 0 0 0 0 Bradford Area SD Brandywine Heights Area SD Brentwood Borough SD Bristol Borough SD Bristol Township SD Brockway Area SD Brookville Area SD Brownsville Area SD Burgettstown Area SD Burrell SD Butler Area SD California Area SD Cambria Heights SD Cameron County SD Camp Hill SD Canon-McMillan SD Canton Area SD Carbondale Area SD Carlisle Area SD Carlynton SD Carmichaels Area SD Catasauqua Area SD Centennial SD Central Bucks SD Central Cambria SD Central Columbia SD Central Dauphin SD Central Fulton SD Central Greene SD Central Valley SD Central York SD Chambersburg Area SD Charleroi SD Chartiers Valley SD Chartiers-Houston SD Cheltenham Township SD Chester-Upland SD Chestnut Ridge SD Chichester SD Clairton City SD Clarion Area SD Clarion-Limestone Area SD Claysburg-Kimmel SD Clearfield Area SD Coatesville Area SD Cocalico SD Colonial SD 0 0 0 1 3 0 0 3 0 0 10 0 0 0 0 0 0 0 0 0 0 0 7 3 0 0 0 0 0 0 0 1 0 7 0 7 24 0 0 6 0 0 0 0 2 0 8 0 0 0 0 6 0 0 1 0 0 12 0 0 0 0 0 0 0 5 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 6 0 0 0 0 0 0 0 4 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Columbia Borough SD Commodore Perry SD Conemaugh Township Area SD Conemaugh Valley SD Conestoga Valley SD Conewago Valley SD Conneaut SD Connellsville Area SD Conrad Weiser Area SD Cornell SD Cornwall-Lebanon SD Corry Area SD Coudersport Area SD Council Rock SD Cranberry Area SD Crawford Central SD Crestwood SD Cumberland Valley SD Curwensville Area SD Dallas SD Dallastown Area SD Daniel Boone Area SD Danville Area SD Deer Lakes SD Delaware Valley SD Derry Area SD Derry Township SD Donegal SD Dover Area SD Downingtown Area SD Dubois Area SD Dunmore SD Duquesne City SD East Allegheny SD East Lycoming SD East Penn SD East Pennsboro Area SD East Stroudsburg Area SD Eastern Lancaster County SD Eastern Lebanon County SD Eastern York SD Easton Area SD Elizabeth Forward SD Elizabethtown Area SD Elk Lake SD Ellwood City Area SD Ephrata Area SD 0 0 0 0 0 0 0 8 0 0 1 0 0 8 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 20 0 1 0 36 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 9 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 6 1 1 0 0 0 1 0 0 4 0 0 1 0 0 0 2 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 Erie City SD Everett Area SD Exeter Township SD Fairfield Area SD Fairview SD Fannett-Metal SD Farrell Area SD Ferndale Area SD Fleetwood Area SD Forbes Road SD Forest Area SD Forest City Regional SD Forest Hills SD Fort Cherry SD Fort LeBoeuf SD Fox Chapel Area SD Franklin Area SD Franklin Regional SD Frazier SD Freedom Area SD Freeport Area SD Galeton Area SD Garnet Valley SD Gateway SD General McLane SD Gettysburg Area SD Girard SD Glendale SD Governor Mifflin SD Great Valley SD Greater Johnstown SD Greater Latrobe SD Greater Nanticoke Area SD Greencastle-Antrim SD Greensburg Salem SD Greenville Area SD Greenwood SD Grove City Area SD Halifax Area SD Hamburg Area SD Hampton Township SD Hanover Area SD Hanover Public SD Harbor Creek SD Harmony Area SD Harrisburg City SD Hatboro-Horsham SD 0 0 0 0 0 0 1 0 6 0 0 0 0 0 0 3 0 2 0 0 0 0 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 2 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 Haverford Township SD Hazleton Area SD Hempfield SD Hempfield Area SD Hermitage SD Highlands SD Hollidaysburg Area SD Homer-Center SD Hopewell Area SD Huntingdon Area SD Indiana Area SD Interboro SD Iroquois SD Jamestown Area SD Jeannette City SD Jefferson-Morgan SD Jenkintown SD Jersey Shore Area SD Jim Thorpe Area SD Johnsonburg Area SD Juniata County SD Juniata Valley SD Kane Area SD Karns City Area SD Kennett Consolidated SD Keystone SD Keystone Central SD Keystone Oaks SD Kiski Area SD Kutztown Area SD Lackawanna Trail SD Lakeland SD Lake-Lehman SD Lakeview SD Lampeter-Strasburg SD Lancaster SD Laurel Highlands SD Laurel SD Lebanon SD Leechburg Area SD Lehighton Area SD Lewisburg Area SD Ligonier Valley SD Line Mountain SD Littlestown Area SD Lower Dauphin SD Lower Merion SD 0 47 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 6 1 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 1 0 Lower Moreland Township SD Loyalsock Township SD Mahanoy Area SD Manheim Central SD Manheim Township SD Marion Center Area SD Marple Newtown SD Mars Area SD McGuffey SD McKeesport Area SD Mechanicsburg Area SD Mercer Area SD Methacton SD Meyersdale Area SD Mid Valley SD Middletown Area SD Midd-West SD Midland Borough SD Mifflin County SD Mifflinburg Area SD Millcreek Township SD Millersburg Area SD Millville Area SD Milton Area SD Minersville Area SD Mohawk Area SD Monessen City SD Moniteau SD Montgomery Area SD Montour SD Montoursville Area SD Montrose Area SD Moon Area SD Morrisville Borough SD Moshannon Valley SD Mount Carmel Area SD Mount Pleasant Area SD Mount Union Area SD Mountain View SD Mt Lebanon SD Muhlenberg SD Muncy SD Nazareth Area SD Neshaminy SD Neshannock Township SD New Brighton Area SD New Castle Area SD 0 0 0 0 6 0 0 2 1 6 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 1 0 0 0 19 0 0 0 0 0 0 0 0 0 3 0 1 0 0 9 0 5 0 0 0 0 0 0 0 0 1 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 New Hope-Solebury SD New Kensington-Arnold SD Newport SD Norristown Area SD North Allegheny SD North Clarion County SD North East SD North Hills SD North Penn SD North Pocono SD North Schuylkill SD North Star SD Northampton Area SD Northeast Bradford SD Northeastern York SD Northern Bedford County SD Northern Cambria SD Northern Lebanon SD Northern Lehigh SD Northern Potter SD Northern Tioga SD Northern York County SD Northgate SD Northwest Area SD Northwestern SD Northwestern Lehigh SD Norwin SD Octorara Area SD Oil City Area SD Old Forge SD Oley Valley SD Oswayo Valley SD Otto-Eldred SD Owen J Roberts SD Oxford Area SD Palisades SD Palmerton Area SD Palmyra Area SD Panther Valley SD Parkland SD Pen Argyl Area SD Penn Cambria SD Penn Hills SD Penn Manor SD Penncrest SD Penn-Delco SD Pennridge SD 3 0 0 0 2 0 0 2 19 0 0 0 7 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 4 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 22 0 0 0 0 Penns Manor Area SD Penns Valley Area SD Pennsbury SD Penn-Trafford SD Pequea Valley SD Perkiomen Valley SD Peters Township SD Philadelphia City SD Philipsburg-Osceola Area SD Phoenixville Area SD Pine Grove Area SD Pine-Richland SD Pittsburgh SD Pittston Area SD Pleasant Valley SD Plum Borough SD Pocono Mountain SD Port Allegany SD Portage Area SD Pottsgrove SD Pottstown SD Pottsville Area SD Punxsutawney Area SD Purchase Line SD Quaker Valley SD Quakertown Community SD Radnor Township SD Reading SD Red Lion Area SD Redbank Valley SD Reynolds SD Richland SD Ridgway Area SD Ridley SD Ringgold SD Riverside Beaver County SD Riverside SD Riverview SD Rochester Area SD Rockwood Area SD Rose Tree Media SD Saint Clair Area SD Saint Marys Area SD Salisbury Township SD Salisbury-Elk Lick SD Saucon Valley SD Sayre Area SD 0 0 3 0 0 0 0 85 0 0 0 1 59 9 23 2 25 0 0 0 0 0 0 0 0 1 1 56 0 0 0 0 0 0 2 1 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 501 0 0 0 0 21 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 1 0 0 0 0 0 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Central Fulton SD Central Greene SD Central Valley SD Central York SD Chambersburg Area SD Charleroi SD Chartiers Valley SD Chartiers-Houston SD Cheltenham Township SD Chester-Upland SD Chestnut Ridge SD Chichester SD Clairton City SD Clarion Area SD Clarion-Limestone Area SD Claysburg-Kimmel SD Clearfield Area SD Coatesville Area SD Cocalico SD 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 1 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 1 1 0 0 13 56 0 0 29 0 3 8 1 0 0 0 4 0 11 9 7 0 6 9 5 2 0 118 1 3 1 5 68 14 8 4 90 300 0 18 14 0 1 1 2 2 0 0 1 0 0 3 12 0 0 0 0 0 0 0 0 0 1 0 0 6 2 0 0 5 13 4 0 0 36 0 0 1 0 28 2 2 0 2 25 0 1 0 0 0 0 2 3 1 Colonial SD Columbia Borough SD Commodore Perry SD Conemaugh Township Area SD Conemaugh Valley SD Conestoga Valley SD Conewago Valley SD Conneaut SD Connellsville Area SD Conrad Weiser Area SD Cornell SD Cornwall-Lebanon SD Corry Area SD Coudersport Area SD 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Intersections between Education and Juvenile Justice

Student’s Name
Institution
Course’s Name
Instructor’s Name
Date

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Intersections between Education and Juvenile Justice
Cases of juvenile incarceration, suspensions, and expulsions have been rising in the
United States. A 2020 report by American Civil Liberties Union (ACLU) indicates
approximately 60,000 youths under 18 years are incarcerated in the U.S’s juvenile prisons and
jails daily. According to ACLU (2020), juvenile incarceration interrupts youths’ educations and
exposes them to psychological and physical problems, such as violence, trauma, suicidal
ideations, depression, and stress. As a result, it negatively impacts their lifelong development and
growth. In the past 50 years, the intersection between public education and juvenile justice has
tightened. The tightening of this intersection has triggered adverse consequences to juveniles,
their families, and the country. Studies have shown that marginalized students from
underrepresented groups, including Hispanics, Alaska Natives, Native Americans, and African
Americans, often suffer disproportionately from these adverse trends. This research focuses on
the broad question, “What is the connection between education policies and students’
involvement in the juvenile justice system (JJS)?”
Existing studies have shown that education systems adapted by different states have
different impacts on juvenile justice and children’s lifelong development after incarceration. The
“zero-tolerance” policies have been cited as the primary cause of the rising juvenile incarceration
and delinquency. “Zero tolerance” policies have sanctioned the use of severe consequences, such
as one-year suspension for students found in possession of drugs, dress-code violations,
tardiness, truancy, sharp objects, alcohol, and drugs. Several cases have been documented where
students have been expelled or suspended for possession of squirt guns, fingernail clipper, pocket
knives, and even drawing images of weapons. Youth advocacy groups and scholars have
vehemently criticized “zero tolerance” and “zero violence” policies, arguing that they are

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counterproductive, ineffective, targeted on specific races, and push students into the criminal
justice systems.
“Zero tolerance” and “zero violence” trends, practices, policies, and laws have tightened
the interconnection between JJS and education systems by indirectly or directly triggering more
youths to involve in the juvenile justice system, either as children or at their adulthood. Children
are directly involved in the juvenile system when school authorities refer them to law
enforcement for indulging in wrongful behaviors or when law enforcers arrest them for
misconduct. This happens to many students yearly, even for minor or nonviolent offenses, such
as “disturbing school activities, “interference with the school process,” failure to abide by school
instructions,” and “disorderly conduct.” Several students have been arrested for using perfume in
class, chatting in class, burping in class, pouring water on other students, and even nicknaming
their teachers.
Education systems’ increased use of “zero tolerance” policies and severe punishments
have had a tremendous indirect impact on students’ increased involvement in JJS. Every year,
thousands of students are suspended, and some get expelled. Many of these expulsions and
suspensions arise from minor and nonviolent violations of school regulations and not from
offenses that risk other students’ well-being. Expulsions from school are strongly linked to
students’ immediate involvement in juvenile justice. When children are left unmonitored or not
in school, they are likely to indulge in delinquent acts that trigger arrests. Similarly, existing
studies have shown a direct correlation between students’ suspensions and poor performance in
school, dropping out of school, and failure to graduate.
This study will answer the following well-framed, predictive research questions: (1) Do
“zero tolerance” and “zero violence” policies push students into juvenile justice systems? (2)

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How do exclusionary disciplinary approaches affect the students physically and psychologically?
(3) What is the connection between students’ expulsions and suspensions with their involvement
in the justice systems? (4) What are the consequences of tightened connection between education
systems and juvenile justice?
The problems of rising juvenile incarceration, suspensions, and expulsions would be
addressed by first establishing the connection between education and JJS, then developing
effective solutions. The research questions are related to the problem because they are centered
on depicting the correlation between education and juvenile justice systems. By depicting this
correlation, it will make it easier to develop advanced solutions that are effective, reliable, and
efficient. An example of a likely solution is the creation of a conducive learning environment and
the development of fair and transparent rules.
Visualizations
In this study, data is visualized using a univariate histogram and a multi-variate scatter
plot. A univariate histogram is a type of graph that organizes data points and datasets into userspecified ranges. A histogram is used in this research because it helps in representing the juvenile
expulsion and suspension based on diverse acts and policies across diverse schools in the U.S.
By doing so, it makes it easier to understand the education systems that are mostly affected by
juvenile incarceration. Further, the histogram assists in representing racial groups that are
affected mainly by suspension and expulsion. The existing data has shown that Black American
students are affected mainly by suspensions and expulsions than any other students. Therefore,
the histogram will assist in ascertaining the validity and credibility of existing data.
Moreover, the univariate histogram is used because it works more effectively with large
data ranges. In this study, the data ranges included data related to students’ enrollment, number

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of schools, misconduct incidents tabulated, number of offenders, incidents involving local law
enforcement, total incarcerations, and assignments to alternative education. As a result, it makes
it easier to compare and contrast data. By comparing the data, one can make informed decisions
grounded on facts and not opinions. Specifically, the histogram will assist in presenting a clear
linkage between education systems and juvenile incarcerations categorized based on students’
gender, race, and age.
Also, a univariate histogram is employed because it offers a more concrete form of data
consistency because data intervals tend to be equal. The data consistency will make it easier to
transform the data from frequency tables to histograms. As a result, it will save on time and
energy incurred in data transformation and data analysis. The histogram also makes it easier to
understand the process line of the data relating to students’ expulsion and suspension. By
understanding the process line, it will make it easier to know the race of students who are most
affected by suspension and expulsion and the education systems that have a high prevalence of
expulsion and suspension.
A multi-variate scatter plot is chosen because it helps in understanding the correlation
between juvenile incarcerations and their races. Existing studies have shown that black students
are mostly incarcerated than white students. Therefore, the scatter plot will assist in ascertaining
the validity and credibility of existing data. On the x-axis, data related to black students’
violence, suspension, and expulsion will be plotted. On the y-axis, data related to white students’
violence, suspension, and expulsion will be plotted. If the data is predominately skewed towards
the y-axis, it will depict that white children are more affected by juvenile incarceration.
However, if the plot is skewed towards the x-axis, it will show that black students are the victims
of juvenile delinquency and incarceration.

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The scatter plot is leveraged because it assists in determining the data flow’ ranges, like
the minimum and maximum values. For example, it assists in determining the maximum number
of black juveniles incarcerated over a given period. As a result, it makes it easier to identify the
trends of incarceration of students over time. Apart from depicting the trends, the scatter plot is
used to depict the non-linear pattern in the data. The non-linear pattern is fundamental in
identifying the point of divergence in data. Identifying the data patterns makes it easier to
attribute types of education systems to students’ suspension and expulsion. Finally, the scatter
plot will help in disapproving or proving the relationship between education systems’ mode of
expulsion and suspension and juvenile systems.
Communicating Insights
The data will be analyzed first using an Excel sheet before being communicated. First,
the data will be documented in an Excel document based on the students’ enrollment, number of
schools, misconduct incidents tabulated, number of offenders, incidents involving local law
enforcement, total incarcerations, and assignments to alternative education. After putting data in
Excel cells and columns, Excel’s analysis features would be used. The Excel will assist in
generating histograms, scatter plots, and even bar graphs. After generating these types of
visualization, the next step is the communication of the findings.
In communicating insights, I would focus on critical takeaways to elicit interest from the
targeted audiences, including students, their families, and policymakers. I would use a
PowerPoint document to summarize key takeaways. The PowerPoint will allow me to
incorporate very attractive images, graphs, and charts that have the audiences’ greater visual
impact. Also, I would frame my data communication for students, their families, and
policymakers. For students, the communication will focus on helping them understand how

7
juvenile justice systems affect their physical and psychological subsets, including emotions,
anxiety, depression, and self-harm behaviors. I would communicate data for their families that
help them understand how their children’s educational systems (suspensions and expulsions)
affect them. For policymakers, I would communicate recommendations that can be implemented
to minimize the adverse consequences of juvenile systems on students.
Further, I would communicate my findings by using short written narratives, charts,
graphs, and numbers. Studies have shown that it is easier for the audience to understand and
remember information presented using graphs than reading the entire text. Therefore, I would
invest in visuals in communicating my insights. I understand that visuals provide a greater way
of communicating information because they are direct ad quick, and they are usually easy to
understand, memorable, and they are more likely to win readers’ interest and attention. Finally, I
would use clear headings and subheadings to make crucial points stand out.
Presentation
In the United States, thousands of students are expelled or suspended yearly for engaging
in nonviolent and petty offenses in school. The rising cases of students’ suspensions and
expulsions can be attributed to ineffective and counterproductive policies that focus on punishing
students rather than corrected them. “Zero tolerance” policies adopted by most education
systems are the major causes of rising cases of students’ involvement in juvenile systems. Many
cases have been reported where students are arrested for engaging in minor offenses, such as
scolding others, nicknaming their teachers, absconding their duties in school, being in possessing
of knives, and even not wearing the right uniforms. Therefore, this study tries to address the
broad question, “What is connection between education policies and students’ involvement in the
JJS?”

8
A study by Ruben (2018) tightened intersection between education and JJS have huge
negative consequences on students, their family members, and the country. Ruben (2018) argued
that students’ involvement in JJS is the major severe consequence that increases the risks of
children incarcerations. The repercussions of youths’ incarceration include more limited housing,
military, employment, and educational opportunities in the future. Further, juvenile
incarcerations reinforce students’ violent behaviors and attitudes and cause mental problems.
Once incarcerated, a student cannot concentrate in school, thus resulting in poor performance,
risk of school dropout, and not completing their studies.
Joseph (2019) stated that juvenile incarceration increases cases of stigmatization,
victimization, embarrassment, and trauma. In most cases, students who have been arrested in the
past are more likely to be arrested by law enforcers stationed at school. Many of these students
are arrested for offenses they have not committed. False victimization often causes stress,
trauma, anxiety, depression, and suicidal ideations. Joseph (2019) further demonstrated that
arresting a child, even...


Anonymous
Excellent resource! Really helped me get the gist of things.

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