2f:T933,(2024 update of this page in progress) http://nevadareportcard.nv.gov/DI/nv/2024
(Previous year: http://nevadareportcard.nv.gov/DI/nv/2023) * Chronic absenteeism data * Proficiency data Nevada Independent article: https://thenevadaindependent.com/article/chronic-absenteeism-decreases-at-nevada-schools-proficiency-rates-improve
2023 article: https://thenevadaindependent.com/article/despite-in-person-learning-return-nevada-schools-saw-continued-proficiency-loss-last-year Unfortunately, the article did not mention attendance or absenteeism, which may be the most important factor for the proficiency losses during the pandemic. But the Indy did publish this article a few weeks later: https://thenevadaindependent.com/article/more-than-a-third-of-nevada-students-chronically-absent-continuing-pandemic-era-trend And, they addressed chronic absenteeism in this article a couple of months earlier: https://thenevadaindependent.com/article/pandemics-effect-on-education-outcomes-rages-on An Indy article from 2019: https://thenevadaindependent.com/article/its-not-a-kid-issue-why-an-elementary-school-is-using-home-visits-virtual-reality-to-combat-chronic-absenteeism Some more links: * Chronic Absenteeism and Disrupted Learning Require an All-Hands-on-Deck Approach https://www.whitehouse.gov/cea/written-materials/2023/09/13/chronic-absenteeism-and-disrupted-learning-require-an-all-hands-on-deck-approach/ * Absences Add Up: New Chronic Absenteeism Campaign Works to Get Kids to Class, and to Graduation https://obamawhitehouse.archives.gov/blog/2016/02/24/absences-add-new-chronic-absenteeism-campaign-works-get-kids-class-and-graduation 30:T4b5,{ //let x = d=>Number(d["Chronic Absenteeism - All Students"]); let x = d=>Number(d["Chronic Absenteeism"]); //let y = d=>Number(d["Black %"]); //let x = d=>Number(d["Hispanic %"]); let y = proficiencyScoreFunction(proficiencySelection1); let yLabel = proficiencyLabel(proficiencySelection1); return Plot.plot({ aspectRatio: 1, width: 400, color: { legend: true, label: selectColorBy, scheme: "Warm", }, x: { axis: 'bottom', grid: 10, label: "Chronic absenteeism %", domain: [0, 100], }, y: { grid: 10, label: yLabel }, //symbol: {legend: true}, marks: [ Plot.ruleY([0]), Plot.ruleX([0]), Plot.ruleY([100]), Plot.ruleX([100]), Plot.dot(selectedData, { //x: d=>Number(d["% Proficient ELA"]), x, //y: d=>Number(d["% Proficient Math"]), //y: d=>Number(d["% Proficient ELA"]), y, r: 2, title: "School Name", //symbol: d=>d["School Name"].slice(-2), stroke: d=>Number(d[selectColorBy])||d[selectColorBy], strokeOpacity: d=> isMatchingSchoolName(d) ? 1 : 0.1, tip: true, }), Plot.linearRegressionY(selectedData, {x, y}), ] }); }31:T46e,{ let y = d=>Number(d["Chronic Absenteeism - All Students"]); let x = "Accountability Year"; let data = chronicAbsenteeismThru2024.filter(d=>d["Organization Code"] < 100); //let data = absenteeism.filter(d=>d["Organization Code"].length==2); let stroke = d=>d.Name; let highlightStrokes = {State:1,Clark:1,Washoe:1,Esmeralda:1, Douglas:0,Mineral:0,Eureka:0, Elko:0, "State Charters":1}; let textData = data.filter(d=>d[x]==="2023-2024" && highlightStrokes[stroke(d)]); return Plot.plot({ width, color: { legend: true, scheme: "rainbow", }, marginRight: 50, x: { axis: 'bottom', grid: 10, label: "School Year", }, y: { grid: 10, label: "Chronic absenteeism %" }, marks: [ Plot.ruleY([0]), Plot.textY(textData, { x, y, dx: 5, fill: stroke, text: stroke, textAnchor: "start", fontWeight: d=>3, fontSize: 15, }), Plot.lineY(data, { x, y, stroke, strokeWidth: d=>highlightStrokes[stroke(d)] ? 5: 1, }), ] }); }32:T574,{ let x = "Accountability Year"; let marks = []; //let data = absenteeismByGroup; let data = chronicAbsenteeismThru2024.filter(d=>d.Name == "State"); let lastYear = d3.max(data,d=>d[x]) console.log(lastYear); let columns = Object.keys(data[0]).filter(d=>d.startsWith("Chronic")); columns.forEach((column,i)=>{ let label = column.split("-")[1].slice(1); console.log(label); let y = d=>Number(d[column]); marks.push( Plot.lineY(data, { x, y, stroke: d=>label+ ".", title: d=>label, }) ); marks.push( Plot.dot(data, { x, y, stroke: d=>label+ ".", fill: d=>label+ ".", title: d=>label, }) ); marks.push( Plot.text(data.filter(d=>d[x]===lastYear), { x, y, text: (d) => "" + y(d) + " " + label.replace("/"," /\n"), dx: 5, dy: {EL:10,FRL:3,Hispanic:-5}[label]||0, fill: d=>label+ ".", lineAnchor: "middle", textAnchor: "start", }) ); }); return Plot.plot({ width, marginRight:100, color: { //legend: true, scheme: "rainbow", }, x: { axis: 'bottom', grid: 10, label: "School year", }, y: { grid: 10, label: "Chronic absenteeism %" }, marks: [ Plot.ruleY([0]), ...marks ] }); }33:T6ff,{ let years = { "2018-2019":1, "2020-2021":2, "2021-2022":4, "2022-2023":6, }; let data = crtByGroupWithAbsenteeism.filter(d=>d.absenteeism && years[d.Year]); //let data = filteredData; let x = d => d&&Number(d.absenteeism); let y = proficiencyScoreFunction(proficiencySelection); let yLabel = proficiencyLabel(proficiencySelection); let stroke = d=>d.Group; //let stroke = d=>d.Name.slice(1); return Plot.plot({ aspectRatio: 1, width: width/2, color: { //legend: true, scheme: "rainbow", }, x: { axis: 'bottom', grid: 10, label: "Chronic absenteeism %", domain: [0, 75], }, y: { grid: 10, label: yLabel, domain: [0, 75], }, marks: [ Plot.ruleY([0]), Plot.ruleX([0]), Plot.ruleY([100]), Plot.ruleX([100]), Plot.dot(data, { x, y, r: d=>years[d.Year], stroke, }), Plot.dot(data.filter(d=>d.Year==="2022-2023"), { x, y, r: d=>years[d.Year], fill:stroke, }), Plot.line(data, { x:x, y:y, //x2:(d,i)=>x(data[i+1]), //y2:(d,i)=>y(data[i+1]), stroke }), Plot.text(data.filter(d=>d.Year==="2018-2019"), { x, y, text: (d) => "2018-2019", dx: -5, dy: 0,//{EL:10,FRL:3,Hispanic:-5}[label]||0, fill: stroke, lineAnchor: "bottom", textAnchor: "end", }), Plot.text(data.filter(d=>d.Year==="2021-2022"), { x, y, text: stroke, dx: 8, dy: 0,//{EL:10,FRL:3,Hispanic:-5}[label]||0, fill: stroke, lineAnchor: "bottom", textAnchor: "start", }) ] }); }34:T853,{ let years = { "2018-2019":1, "2020-2021":2, "2021-2022":3, "2022-2023":4, "2023-2024":6, }; let r = d=>years[d[yearCol]]; let yearCol = "Accountability Year"; let x = d => { let year = d&&Number(d[yearCol].split("-")[0]); //console.log(y); if (year > 2023) { console.log(d); } return year; }; //let data = proficiencyByYear.filter(d=>x(d) && d["Organization Code"].startsWith("0223") ); //let data = proficiencyByYear.filter(d=>x(d) && d["Organization Code"].length == 2); //let data = filteredData; let y = proficiencyScoreFunction(proficiencySelection); let schoolLevel = "Elementary"; let data = schoolPerformanceThru2024.filter(d=>x(d) && y(d) && Number(d["Organization Code"]) < 100 && d["School Level"] === schoolLevel && d.Subgroup === "Total"); let stroke = d=>d.Name; console.log("data",data); console.log("x",data.map(x)); console.log("y",data.map(y)); console.log("r",data.map(r)); console.log("stroke",data.map(stroke)); let yLabel = proficiencyLabel(proficiencySelection); //let stroke = d=>d.Name.slice(1); return Plot.plot({ //aspectRatio: 1, width: width, marginRight: 100, // space to the left of the chart color: { //legend: true, scheme: "rainbow", }, x: { axis: 'bottom', grid: 4, label: "Year", //domain: [0, 75], }, y: { grid: 10, label: yLabel, //domain: [0, 75], }, marks: [ Plot.dot(data, { x, y, r, stroke, }), Plot.dot(data.filter(d=>d[yearCol]==="2023-2024"), { x, y, r, fill:stroke, }), Plot.line(data, { x, y, //x2:(d,i)=>x(data[i+1]), 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2022-2023","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":420,"value":"viewof proficiencySelection1 = Inputs.radio([\"Math\", \"English\", \"Average\"], {label: \"Select proficiency score\", value: \"Average\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":441,"value":"viewof checkboxesLevel = Inputs.checkbox([\"ES\", \"MS\",\"HS\"], {label: \"Select school levels\", value: [\"ES\", \"MS\",\"HS\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":507,"value":"viewof checkboxesType = Inputs.checkbox(Object.keys(schoolTypeCounts), {label: \"Select school types\", value: [\"Regular\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":82,"value":"viewof districtSelected = Inputs.select([\"All\",...districtNames], {label: \"Select district\", value:\"Clark\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":407,"value":"viewof selectColorBy = Inputs.select(Object.keys(ratingsAbsenteeism[0]), {label: \"Color by\",value:\"level\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":452,"value":"viewof schoolNameInput = Inputs.text({label: \"School name search\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"First 20 matching schools: ${getMatchingSchoolNames().slice(-20).join(\", \")}","pinned":false,"hidden":false,"mode":"md"},{"id":285,"value":"---\n### Chronic absenteeism rose sharply in Clark and Washoe and some other districts after COVID-19 hit, but has been improving\nSome rural counties saw a chronic absenteeism decline in 2020-2021, but absenteeism rose later. For almost all districts, absenteeism is still much higher than in 2018-2019.","pinned":false,"hidden":false,"mode":"md"},{"id":160,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":289,"value":"---\n### Changes in chronic absenteeism by race and ethnicity.\nAll groups saw steep increases in chronic absenteeism since 2018-2019, but there was a slight improvement in 2022-2023.","pinned":false,"hidden":false,"mode":"md"},{"id":175,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":292,"value":"----\n### Proficiency vs. chronic absenteeism by group and year, 2018-2019, 2020-2021, 2021-2022, and 2022-2023","pinned":false,"hidden":false,"mode":"md"},{"id":687,"value":"schoolPerformanceThru2024.filter(d=>d.Name == \"State\");","pinned":true,"hidden":false,"mode":"ojs"},{"id":369,"value":"viewof proficiencySelection = Inputs.radio([\"Math\", \"English\", \"Average\"], {label: \"Select proficiency score\", value: \"Average\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":600,"value":"### Proficiency by year and district","pinned":false,"hidden":false,"mode":"md"},{"id":717,"value":"schoolPerformanceThru2024.filter(d=>d[\"Organization Code\"] < 100 && d.Subgroup === \"Total\").map(proficiencyScoreFunction(\"Average\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":549,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":423,"value":"function proficiencyScoreFunction(selection) {\n let math = d=>d[\"% Proficient Math\"] || d[\"Mathematics - % Proficient\"];\n let ela = d=>d[\"% Proficient ELA\"] || d[\"ELA - % Proficient\"];\n let useMath = {Math:1,Average:1}[selection]|| 0;\n let useEla = {English:1,Average:1}[selection]|| 0;\n let y = d => d&&(useMath * Number(math(d)) + useEla*Number(ela(d)))/(useMath+useEla);\n return y; \n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":425,"value":"function proficiencyLabel(selection) {\n return {\n Math: \"Math proficiency %\",\n English: \"English proficiency %\",\n Average: \"Math + ELA average proficiency %\",\n }[selection];\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":218,"value":"----\nSome links about the relationship between absenteeism and performance:\n\n* https://www.epi.org/publication/student-absenteeism-who-misses-school-and-how-missing-school-matters-for-performance/\n* https://nces.ed.gov/pubs2009/attendancedata/chapter1a.asp#:~:text=Students%20who%20attend%20school%20regularly,in%20a%20child's%20school%20career.\n* https://soeonline.american.edu/blog/importance-of-school-attendance/\n* https://www.attendanceworks.org/chronic-absence/the-problem/10-facts-about-school-attendance/\n* https://www.brookings.edu/articles/school-attendance-a-building-block-of-student-achievement/\n ","pinned":false,"hidden":false,"mode":"md"},{"id":75,"value":"----\n## Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":77,"value":"Absenteeism data\nhttp://nevadareportcard.nv.gov/di/report/reportcard_1?report=reportcard_1&scope=e33.y16.y18.y19.y20&organization=c29417&fields=309%2C310%2C311%2C313%2C318%2C320&hiddenfieldsid=309%2C310%2C311%2C313%2C318%2C320&scores=1446&num=160&page=1&pagesize=20&domain=chronicabsenteeism&\n","pinned":false,"hidden":false,"mode":"md"},{"id":80,"value":"school data http://nevadareportcard.nv.gov/DI/nv/2023","pinned":false,"hidden":false,"mode":"md"},{"id":459,"value":"function isMatchingSchoolName(d, t=schoolNameInput) {\n t = t.toLocaleLowerCase();\n return d[\"School Name\"].toLocaleLowerCase().includes(t);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":463,"value":"function getMatchingSchoolNames(a=selectedData,t=schoolNameInput) {\n return a.filter(d=>isMatchingSchoolName(d,t)).map(d=>d[\"School Name\"]);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"filteredData = ratingsAbsenteeism;","pinned":false,"hidden":false,"mode":"ojs"},{"id":515,"value":"ratingsAbsenteeism.filter(d=>!Number(d[\"Chronic Absenteeism - All Students\"]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":535,"value":"ratingsAbsenteeism.filter(d=>Number(d[\"Total Enrollment\"]) < 20)","pinned":true,"hidden":false,"mode":"ojs"},{"id":522,"value":"ratingsAbsenteeism.filter(d=>!Number(d[\"Chronic Absenteeism\"]) && Number(d[\"Chronic Absenteeism - All Students\"]) )","pinned":true,"hidden":false,"mode":"ojs"},{"id":398,"value":"selectedData = {\n return filteredData.filter(d=> checkboxesType.includes(d[\"School Type\"]) && checkboxesLevel.includes(d.level) &&\n (districtSelected === \"All\" || d[\"District Name\"]==districtSelected ));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":173,"value":"absenteeismByGroup = d3.csvParse((await FileAttachment(\"Chronic-absenteeism.csv\").text()))","pinned":false,"hidden":false,"mode":"ojs"},{"id":228,"value":"crtByGroup = d3.csvParse((await FileAttachment(\"crtGroupSummary@1.csv\").text()))","pinned":true,"hidden":false,"mode":"ojs"},{"id":231,"value":"crtByGroupWithAbsenteeism = {\n let a = crtByGroup.map(d=> {\n let r = absenteeismByGroup.find(r=>r[\"Accountability Year\"]===d.Year); \n let absenteeism = \"\";\n if (r) {\n let g = d.Group;\n if (g === \"State\") {\n absenteeism = r[\"Chronic Absenteeism - All Students\"]; \n }\n else if (g === \"Am In/AK Native\") {\n absenteeism = r['Chronic Absenteeism - American Indian/Alaskan Native'];\n }\n else {\n let k = \"Chronicabsenteeism - \" + g;\n absenteeism = r[k]; \n if (!absenteeism) {\n console.log(\"No absenteeism match\",{g,k}); \n }\n }\n }\n return {\n ...d,\n absenteeism\n };\n });\n return a;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"absenteeism = d3.csvParse((await FileAttachment(\"Report.csv\").text()).split(\"\\r\\n\").slice(2).join(\"\\n\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":733,"value":"schoolPerformanceThru2024.filter(d=>d[\"Organization Code\"])","pinned":true,"hidden":false,"mode":"ojs"},{"id":639,"value":"schoolPerformanceThru2024 = d3.csvParseRows((await FileAttachment(\"2024 NV school performance.csv\").text()).split(\"\\n\").slice(3).join(\"\"),d => {\n let o = {};\n if (d)\n d3.autoType(d).forEach((c,j)=> o[performanceColumns[j]] = c );\n return o;\n});","pinned":true,"hidden":false,"mode":"ojs"},{"id":757,"value":"performanceColumns = [\"Name\",\"Accountability Year\",\"Organization Code\",\"School Level\",\"Subgroup\",\n \"ELA - % Proficient\",\"ELA - MIP\",\"ELA - LTG\",\"Mathematics - % Proficient\",\"Mathematics - MIP\",\"Mathematics - LTG\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":708,"value":"chronicAbsenteeismThru2024 = d3.csvParse((await FileAttachment(\"2024 NV chronic absenteeism by school with groups.csv\").text()).split(\"\\n\").slice(2).join(\"\"),d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":539,"value":"proficiencyByYear = d3.csvParse((await FileAttachment(\"Proficiency by year and school grades 3-8.csv\").text()).split(\"\\r\\n\").slice(3).join(\"\\n\")).map(d=>(\n {\n ...d,\n Group: d.Group.replaceAll('\"','').replaceAll('`',''),\n }\n))","pinned":false,"hidden":false,"mode":"ojs"},{"id":156,"value":"absenteeism2022 = absenteeism.filter(d=>d[\"Accountability Year\"]==\"2022-2023\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"schoolRatings = (await FileAttachment(\"SchoolRatings.csv\").csv()).map(d=>{\n return {\n ...d,\n \"Organization Code\":d[\"NSPF School Code\"].slice(0,5)\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":479,"value":"demographicProfiles = d3.csvParse((await FileAttachment(\"Demographic_Profile.csv\").text()).split(\"\\r\\n\").slice(3).join(\"\\n`\")).map(d=>({...d,Name:d.Name.replace('\"',\"\").replace('`',\"\")}))","pinned":true,"hidden":false,"mode":"ojs"},{"id":55,"value":"import {tidy, leftJoin, count} from '@pbeshai/tidyjs'","pinned":true,"hidden":false,"mode":"ojs"},{"id":53,"value":"ratingsAbsenteeism = tidy(tidy(schoolRatings,leftJoin(absenteeism2022,{by:\"Organization Code\"})).map(d=>({...d,level:d[\"School Name\"].slice(-2)})),leftJoin(demographicProfiles,{by:\"Organization Code\"}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"districtCountsArray = tidy(ratingsAbsenteeism, count(\"District Name\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":92,"value":"districtCounts = valueCounts(ratingsAbsenteeism,\"District Name\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":502,"value":"schoolTypeCounts = valueCounts(ratingsAbsenteeism,\"School Type\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":106,"value":"function valueCounts(a,column) {\n const o = {};\n a.forEach(d=>{\n let n = d[column];\n o[n] = (o[n] || 0)+1;\n });\n return o;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":113,"value":"ratingsAbsenteeism","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"districtNames = Object.keys(districtCounts);","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}