The document describes a student recruitment software called Student Recruiter by GIS Solutions Inc. It uses geographic data and student profiles to identify new areas for colleges to target recruitment efforts. Student data is analyzed to create profiles of different student types. Maps, tables and reports are generated showing recommended schools and addresses for recruitment that match the student profiles. These outputs can be downloaded and used to guide recruitment goals and increase enrollment, tuition, diversity and profitability.
The statistic is a type of scientific examination that utilizes evaluated models, portrayals, and summaries for a given arrangement of trial information or genuine investigations. Statistics consider systems to assemble, audit, break down, and make determinations from the information.
This document discusses using GIS (Geographic Information Systems) in geography classrooms. It provides examples of free and easy to use GIS tools for students, such as Google Earth. It also discusses the benefits of using GIS to help students explore and analyze geographic data, and recommends starting by finding questions the students want to answer. The document encourages teachers to have students think critically about the maps and data they are presented with.
This document provides an outline for a presentation on GIS for planning and design. It begins with an introduction to GIS and what can be done with the software. A key point is made that GIS allows users to query both tabular and spatial attributes of geographic data. The presentation also includes an interactive software demo and examples of recent planning projects done using GIS. It summarizes that GIS is efficient mapping and analysis software that can incorporate open data, attributes, work at all scales, and provide context for geodesign through advanced layout, design and scripting options.
The document describes a student management system created by a group of students. The system allows authorized users to access academic records of registered students and simplifies operations for educational institutions. It handles student details like personal information, course and college details, and academic records. The system was developed to automate a manual student management process and reduce costs and errors compared to the previous system. It has functionalities like creating, deleting, updating, and searching student records.
Empirical Study on Classification Algorithm For Evaluation of Students Academ...iosrjce
Data mining techniques (DMT) are extensively used in educational field to find new hidden patterns
from student’s data. In recent years, the greatest issues that educational institutions are facing the unstable
expansion of educational data and to utilize this information data to progress the quality of managerial
decisions. Educational institutions are playing a prominent role in the public and also playing an essential role
for enlargement and progress of nation. The idea is predicting the paths of students, thus identifying the student
achievement. The data mining methods are very useful in predicting the educational database. Educational data
mining is concerns with improving techniques for determining knowledge from data which comes from the
educational database. However it has issue with accuracy of classification algorithms. To overcome this
problem the higher accuracy of the classification J48 algorithm is used. This work takes consideration with the
locality and the performance of the student in education in order to analyse the student achievement is high over
schooling or in graduation
This document summarizes an empirical study on using classification algorithms to evaluate students' academic performance. Specifically, it evaluates the J48 and Random Forest algorithms on a dataset of 350 students from an Indian college.
The study finds that the J48 algorithm achieves higher accuracy, precision, recall, and F-measure than Random Forest in classifying students as having low, medium, or high performance based on attributes like school grades, location, and college grades. Performance is evaluated on both the training dataset of 155 computer science students and test dataset of information technology students.
Overall, the study concludes that the proposed use of the J48 algorithm provides superior performance over Random Forest in analyzing students' academic achievement and identifying whether their performance
In this guide, we will walk you through how to get an online master’s in statistics that will help your career to grow in this vast and growing field.
Source: Online Master's Colleges
The statistic is a type of scientific examination that utilizes evaluated models, portrayals, and summaries for a given arrangement of trial information or genuine investigations. Statistics consider systems to assemble, audit, break down, and make determinations from the information.
This document discusses using GIS (Geographic Information Systems) in geography classrooms. It provides examples of free and easy to use GIS tools for students, such as Google Earth. It also discusses the benefits of using GIS to help students explore and analyze geographic data, and recommends starting by finding questions the students want to answer. The document encourages teachers to have students think critically about the maps and data they are presented with.
This document provides an outline for a presentation on GIS for planning and design. It begins with an introduction to GIS and what can be done with the software. A key point is made that GIS allows users to query both tabular and spatial attributes of geographic data. The presentation also includes an interactive software demo and examples of recent planning projects done using GIS. It summarizes that GIS is efficient mapping and analysis software that can incorporate open data, attributes, work at all scales, and provide context for geodesign through advanced layout, design and scripting options.
The document describes a student management system created by a group of students. The system allows authorized users to access academic records of registered students and simplifies operations for educational institutions. It handles student details like personal information, course and college details, and academic records. The system was developed to automate a manual student management process and reduce costs and errors compared to the previous system. It has functionalities like creating, deleting, updating, and searching student records.
Empirical Study on Classification Algorithm For Evaluation of Students Academ...iosrjce
Data mining techniques (DMT) are extensively used in educational field to find new hidden patterns
from student’s data. In recent years, the greatest issues that educational institutions are facing the unstable
expansion of educational data and to utilize this information data to progress the quality of managerial
decisions. Educational institutions are playing a prominent role in the public and also playing an essential role
for enlargement and progress of nation. The idea is predicting the paths of students, thus identifying the student
achievement. The data mining methods are very useful in predicting the educational database. Educational data
mining is concerns with improving techniques for determining knowledge from data which comes from the
educational database. However it has issue with accuracy of classification algorithms. To overcome this
problem the higher accuracy of the classification J48 algorithm is used. This work takes consideration with the
locality and the performance of the student in education in order to analyse the student achievement is high over
schooling or in graduation
This document summarizes an empirical study on using classification algorithms to evaluate students' academic performance. Specifically, it evaluates the J48 and Random Forest algorithms on a dataset of 350 students from an Indian college.
The study finds that the J48 algorithm achieves higher accuracy, precision, recall, and F-measure than Random Forest in classifying students as having low, medium, or high performance based on attributes like school grades, location, and college grades. Performance is evaluated on both the training dataset of 155 computer science students and test dataset of information technology students.
Overall, the study concludes that the proposed use of the J48 algorithm provides superior performance over Random Forest in analyzing students' academic achievement and identifying whether their performance
In this guide, we will walk you through how to get an online master’s in statistics that will help your career to grow in this vast and growing field.
Source: Online Master's Colleges
CS Education in Texas ISDs: Partnerships for SuccessWeTeach_CS
Presentation by Carol Fletcher, Deputy Director of the The University of Texas at Austin Center for STEM Education, and Pauline Dow, Deputy Superintendent San Antonio ISD.
Presented to TASA/TASB conference, Dallas, TX, October 2017.
Preparing Your Students for the Innovation Economy with WeTeach_CS WeTeach_CS
The document discusses preparing students for careers in computer science and the innovation economy through the WeTeach_CS program. It notes that there will be 1 million more computing jobs than graduates by 2020 and that Texas had only 2,103 computer science graduates in 2014. WeTeach_CS provides training to Texas educators, with over 1,350 educators from 697 schools and districts participating. The program aims to increase the number of certified computer science teachers and offers online and in-person professional development courses.
Counselors for Computing - Recruiting for CS jkrauss
Recruiting Students into Computer Science: More participation = greater opportunity. NCWIT slide deck for EDC and CAITE webinar with Massachusetts teachers and counselors
Dawn Chastain official transcript Metropolitan State University of DenverDawn Chastain
This official transcript from Metropolitan State University of Denver documents that Dawn Chastain earned a Bachelor of Arts degree in Special Education with a concentration in Linguistically Diverse Education in December 2015. It shows a cumulative GPA of 3.59 based on 150 credit hours completed, most of which were at MSU Denver. The transcript details individual courses, grades, credits, terms of enrollment, and transfer credits accepted from other institutions. It includes information about authenticating the transcript and guidelines for interpreting transcripts from MSU Denver.
Latino Summit Presentation: Houston Community College Building a Pipeline of ...Houston Community College
This document discusses strategies used by Houston Community College to build a pipeline of STEM success for Hispanic students. It notes that HCC graduates more Hispanic students with associate degrees than any other community college in the US. HCC focuses on STEM programs in fields with high demand like engineering, health sciences, and technology. Partnerships with 4-year universities and industry allow students to seamlessly transition from an associate's to a bachelor's degree or into well-paying jobs.
The document provides an overview of Dr. Kurt Ewen's presentation on HCC's strategic planning process. The key points are:
1) The strategic planning process will intentionally involve trustees and support HCC's role in meeting Houston's educational needs.
2) An overview of the conceptual framework for strategic planning shows it occurring at institutional, college, program, and individual levels and being informed by assessment data.
3) A timeline outlines the 18-month planning process, from designing the engagement plan in February/March 2018 to announcing the new plan in June/July 2019.
WIN Learning Career Readiness System OverviewWin Learning
This document discusses the changing labor market and the need for career readiness education. It notes that a high school diploma is no longer sufficient, with nearly 60% of today's workforce having some college education. However, only 40% of 27-year-olds have an associate's degree or higher. The document then introduces a career readiness system that provides resources to help link education to local economic demands through personalized career paths, data analysis, and foundational skills courseware. It discusses implementing this system to help ensure all learners are career and college ready.
Presented at the CS4TX Statewide Meeting, October 19, 2016, in Houston, TX.
Presented by:
Carol Fletcher, Ph.D.
Deputy Director
Center for STEM Education
The University of Texas at Austin
This document outlines a proposal to use analytics and machine learning to predict student dropout rates and implement targeted interventions. It defines the problem of reactive responses to dropouts. The solution involves analyzing existing student data using statistical techniques to develop a retention model. A pilot program classified 17,000 students as probable dropouts using their data and identified key factors like grades and demographics that influence dropout risk. Testing showed the model could correctly predict around 50% of dropouts. Targeted interventions are proposed for at-risk students to reduce estimated revenue losses of $45 million from dropouts.
The culmination of my LEVEL Data Analytics program ('19) efforts. I advised Tyton Partners on what natural next steps to take for an emerging research study aligning success rates in higher-ed with virtual learning. All advanced analysis was conducted via R.
This contains some important concepts in statistics and methods of research. It is a good material for beginners who plan to explore or write a thesis or dissertation.
The document outlines a collective impact initiative called the Road Map Project aimed at dramatically improving student achievement from early childhood through college/career in South Seattle and South King County. It discusses goals of ensuring students are healthy and ready for kindergarten, supported and successful in school, graduate from high school college and career ready, and earn a college degree or career credential. It also outlines strategies for the initiative including collecting and reporting data, engaging the community, and aligning investments.
The document discusses how employment and salary data is becoming increasingly important for institutions to collect and report. It outlines different available data sources such as federal surveys, alumni surveys, and state reporting requirements. The i3 Group is presented as a solution that uses location-discovery technology and experienced counselors to conduct surveys of alumni and provide detailed student-level response data to institutions. Examples of analyses that can be performed on the data are provided, such as comparing average salaries by employment type or industry, and metrics like net promoter scores. Key benefits of working with i3 Group include high response rates, helping with compliance reporting needs, and enabling continuous improvement.
Helping Prospective Students Understand the Computing DisciplinesRandy Connolly
Presentation at Cannexus 2018 in Ottawa in which we discussed the results of our three-year research project on student understandings of the computing disciplines and described the 32-page full-color booklet for advisers and prospective students.
TI Leadership Summit – WeTeach_CS and you can too!WeTeach_CS
Presentation by Carol Fletcher, Deputy Director of the The University of Texas at Austin Center for STEM Education, to the TI Leadership Summit held by Texas Instruments.
Fall 2017
The Role of CS Departments in The US President’s “CS for All” Initiative: Pan...Mark Guzdial
In January 2016, US President Barack Obama started an initiative to provide CS for All – with the goal that all school students should have access to computing education. Computing departments in higher education have a particularly important role to play in this initiative. It’s in our best interest to get involved, since the effort can potentially improve the quality of our incoming students. CS Departments have unique insights as subject-matter experts to inform the development of standards. We can provide leadership to inform and influence education policy. In this session, we will present a variety of ways in which departments and faculty can support CS for All and will answer audience questions about the initiative. Our goal is to provide concrete positive actions for faculty.
Barbara Ericson spoke on influencing our incoming students and using outreach to improve the number and diversity of students and to improve the number and quality of teachers.
Rick Adrion spoke on CS faculty providing subject-matter expertise to standards efforts. A key role for CS faculty is to help teachers, administrators, and public policy makers to understand what CS is.
Megean Garvin spoke on how CS faculty can provide a leadership role. Faculty have a particular privileged position to draw together diverse stakeholders to advance CS Education.
- Educational data mining analyzes data from educational systems like course management systems to discover patterns and insights.
- This paper uses data from a student survey to predict performance and identify factors influencing it. Classification algorithms like decision trees and Naive Bayes are applied.
- The CART decision tree model achieved the highest accuracy of 40% in predicting student grades, outperforming C4.5, ID3, and CHAID trees. The paper finds multiple personal and social factors influence student performance beyond previous grades.
Using Naviance Data to Drive College & Career ReadinessNaviance
Using Naviance data can help schools focus on college and career readiness outcomes. Schools should (1) use reports to identify careers and colleges to target, (2) focus on measurable outcomes like increasing the percentage of students attending 4-year colleges, and (3) collect relevant career and college planning data from students through activities and surveys to evaluate progress toward goals. Building a data culture requires collaboration across staff and students to make data-driven decisions that improve student success.
Educational Data Mining is a growing trend in case of higher education. The quality of the Educational
Institute may be enhanced through discovering hidden knowledge from the student databases/ data
warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree)
Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major
subject wise, gender wise and category/caste wise. The experimental results may be visualized with
Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.
CS Education in Texas ISDs: Partnerships for SuccessWeTeach_CS
Presentation by Carol Fletcher, Deputy Director of the The University of Texas at Austin Center for STEM Education, and Pauline Dow, Deputy Superintendent San Antonio ISD.
Presented to TASA/TASB conference, Dallas, TX, October 2017.
Preparing Your Students for the Innovation Economy with WeTeach_CS WeTeach_CS
The document discusses preparing students for careers in computer science and the innovation economy through the WeTeach_CS program. It notes that there will be 1 million more computing jobs than graduates by 2020 and that Texas had only 2,103 computer science graduates in 2014. WeTeach_CS provides training to Texas educators, with over 1,350 educators from 697 schools and districts participating. The program aims to increase the number of certified computer science teachers and offers online and in-person professional development courses.
Counselors for Computing - Recruiting for CS jkrauss
Recruiting Students into Computer Science: More participation = greater opportunity. NCWIT slide deck for EDC and CAITE webinar with Massachusetts teachers and counselors
Dawn Chastain official transcript Metropolitan State University of DenverDawn Chastain
This official transcript from Metropolitan State University of Denver documents that Dawn Chastain earned a Bachelor of Arts degree in Special Education with a concentration in Linguistically Diverse Education in December 2015. It shows a cumulative GPA of 3.59 based on 150 credit hours completed, most of which were at MSU Denver. The transcript details individual courses, grades, credits, terms of enrollment, and transfer credits accepted from other institutions. It includes information about authenticating the transcript and guidelines for interpreting transcripts from MSU Denver.
Latino Summit Presentation: Houston Community College Building a Pipeline of ...Houston Community College
This document discusses strategies used by Houston Community College to build a pipeline of STEM success for Hispanic students. It notes that HCC graduates more Hispanic students with associate degrees than any other community college in the US. HCC focuses on STEM programs in fields with high demand like engineering, health sciences, and technology. Partnerships with 4-year universities and industry allow students to seamlessly transition from an associate's to a bachelor's degree or into well-paying jobs.
The document provides an overview of Dr. Kurt Ewen's presentation on HCC's strategic planning process. The key points are:
1) The strategic planning process will intentionally involve trustees and support HCC's role in meeting Houston's educational needs.
2) An overview of the conceptual framework for strategic planning shows it occurring at institutional, college, program, and individual levels and being informed by assessment data.
3) A timeline outlines the 18-month planning process, from designing the engagement plan in February/March 2018 to announcing the new plan in June/July 2019.
WIN Learning Career Readiness System OverviewWin Learning
This document discusses the changing labor market and the need for career readiness education. It notes that a high school diploma is no longer sufficient, with nearly 60% of today's workforce having some college education. However, only 40% of 27-year-olds have an associate's degree or higher. The document then introduces a career readiness system that provides resources to help link education to local economic demands through personalized career paths, data analysis, and foundational skills courseware. It discusses implementing this system to help ensure all learners are career and college ready.
Presented at the CS4TX Statewide Meeting, October 19, 2016, in Houston, TX.
Presented by:
Carol Fletcher, Ph.D.
Deputy Director
Center for STEM Education
The University of Texas at Austin
This document outlines a proposal to use analytics and machine learning to predict student dropout rates and implement targeted interventions. It defines the problem of reactive responses to dropouts. The solution involves analyzing existing student data using statistical techniques to develop a retention model. A pilot program classified 17,000 students as probable dropouts using their data and identified key factors like grades and demographics that influence dropout risk. Testing showed the model could correctly predict around 50% of dropouts. Targeted interventions are proposed for at-risk students to reduce estimated revenue losses of $45 million from dropouts.
The culmination of my LEVEL Data Analytics program ('19) efforts. I advised Tyton Partners on what natural next steps to take for an emerging research study aligning success rates in higher-ed with virtual learning. All advanced analysis was conducted via R.
This contains some important concepts in statistics and methods of research. It is a good material for beginners who plan to explore or write a thesis or dissertation.
The document outlines a collective impact initiative called the Road Map Project aimed at dramatically improving student achievement from early childhood through college/career in South Seattle and South King County. It discusses goals of ensuring students are healthy and ready for kindergarten, supported and successful in school, graduate from high school college and career ready, and earn a college degree or career credential. It also outlines strategies for the initiative including collecting and reporting data, engaging the community, and aligning investments.
The document discusses how employment and salary data is becoming increasingly important for institutions to collect and report. It outlines different available data sources such as federal surveys, alumni surveys, and state reporting requirements. The i3 Group is presented as a solution that uses location-discovery technology and experienced counselors to conduct surveys of alumni and provide detailed student-level response data to institutions. Examples of analyses that can be performed on the data are provided, such as comparing average salaries by employment type or industry, and metrics like net promoter scores. Key benefits of working with i3 Group include high response rates, helping with compliance reporting needs, and enabling continuous improvement.
Helping Prospective Students Understand the Computing DisciplinesRandy Connolly
Presentation at Cannexus 2018 in Ottawa in which we discussed the results of our three-year research project on student understandings of the computing disciplines and described the 32-page full-color booklet for advisers and prospective students.
TI Leadership Summit – WeTeach_CS and you can too!WeTeach_CS
Presentation by Carol Fletcher, Deputy Director of the The University of Texas at Austin Center for STEM Education, to the TI Leadership Summit held by Texas Instruments.
Fall 2017
The Role of CS Departments in The US President’s “CS for All” Initiative: Pan...Mark Guzdial
In January 2016, US President Barack Obama started an initiative to provide CS for All – with the goal that all school students should have access to computing education. Computing departments in higher education have a particularly important role to play in this initiative. It’s in our best interest to get involved, since the effort can potentially improve the quality of our incoming students. CS Departments have unique insights as subject-matter experts to inform the development of standards. We can provide leadership to inform and influence education policy. In this session, we will present a variety of ways in which departments and faculty can support CS for All and will answer audience questions about the initiative. Our goal is to provide concrete positive actions for faculty.
Barbara Ericson spoke on influencing our incoming students and using outreach to improve the number and diversity of students and to improve the number and quality of teachers.
Rick Adrion spoke on CS faculty providing subject-matter expertise to standards efforts. A key role for CS faculty is to help teachers, administrators, and public policy makers to understand what CS is.
Megean Garvin spoke on how CS faculty can provide a leadership role. Faculty have a particular privileged position to draw together diverse stakeholders to advance CS Education.
- Educational data mining analyzes data from educational systems like course management systems to discover patterns and insights.
- This paper uses data from a student survey to predict performance and identify factors influencing it. Classification algorithms like decision trees and Naive Bayes are applied.
- The CART decision tree model achieved the highest accuracy of 40% in predicting student grades, outperforming C4.5, ID3, and CHAID trees. The paper finds multiple personal and social factors influence student performance beyond previous grades.
Using Naviance Data to Drive College & Career ReadinessNaviance
Using Naviance data can help schools focus on college and career readiness outcomes. Schools should (1) use reports to identify careers and colleges to target, (2) focus on measurable outcomes like increasing the percentage of students attending 4-year colleges, and (3) collect relevant career and college planning data from students through activities and surveys to evaluate progress toward goals. Building a data culture requires collaboration across staff and students to make data-driven decisions that improve student success.
Educational Data Mining is a growing trend in case of higher education. The quality of the Educational
Institute may be enhanced through discovering hidden knowledge from the student databases/ data
warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree)
Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major
subject wise, gender wise and category/caste wise. The experimental results may be visualized with
Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.
2. Student Recruiter by GIS Solutions Inc Building a profitable college student population Colleges and Universities … are comprised of a diverse student body … recruited from a variety of geographic locations
3. Student Recruiter by GIS Solutions Inc Expanding recruitment possibilities GSI’s Student Recruiter is designed to help you find new areas for recruitment. Existing “hot spots” for recruiting. New high-probability areas for recruitment
4. Student Recruiter by GIS Solutions Inc Methodology GIS Solutions has 17 years of experience with large geographically-based datasets GIS Solutions owns and/or accesses both public and proprietary datasets including: 1. nationwide map of high school boundaries 2. detailed test score data for thousands of high schools 3. detailed demographic data for schools, neighborhoods and even individual addresses Colleges have detailed information about students , including home addresses GIS Solutions combines student addresses with our geographically referenced datasets to produce student type profiles based on: geography demographics economics PRIZM lifestyle segments These “profiles” are used to find similar high-probability recruiting areas
13. Student Recruiter by GIS Solutions Inc Creating the Enhanced Student Dataset Enhanced Student Dataset Student ID Address GPA Net Tuition School-based Fin. Aid map X map Y High School PRIZM Segment Income % Minority etc. 10001 100 Main Street…. 3.74 9500 2500 -88.855 42.478 Oakdale 41 64000 8 10002 301 Cherry Road … 2.9 5000 7000 -87.124 41.214 Benson 55 102000 14 10003 757 Mill Valley … 4.0 12000 0 -92.411 40.23 North Ridge 47 80000 5 10004 30001 Hwy 97….. 3.5 8000 4000 -90.012 41.754 Springfield 31 82000 24 Apply GIS Solutions technology / methodology to combine the two datasets. The result is the Enhanced Student Dataset which include Map X and Map Y information, allowing the student record to be shown on a map.
14. Student Recruiter by GIS Solutions Inc Enhanced Student Dataset can be mapped Enhanced Student Dataset Student ID Address GPA Net Tuition map X map Y High School PRIZM Segment Income % Minority etc. 10001 100 Main Street…. 3.74 9500 -88.855 42.478 Oakdale 41 64000 8 10002 301 Cherry Road … 2.9 5000 -87.124 41.214 Benson 55 102000 14 10003 757 Mill Valley … 4.0 12000 -92.411 40.23 North Ridge 47 80000 5 10004 30001 Hwy 97….. 3.5 8000 -90.012 41.754 Springfield 31 82000 24 X, Y locations allow student records to be mapped.
15. Student Recruiter by GIS Solutions Inc Develop “Profiles” from Enhanced Dataset Enhanced Student Dataset Student ID Address GPA Tuition Paid School-based Financial Aid map X map Y High School PRIZM Segment Income % Minority etc. 10001 100 Main Street…. 3.74 9500 2500 -88.855 42.478 Oakdale 41 64000 8 10002 301 Cherry Road … 2.9 5000 7000 -87.124 41.214 Benson 55 102000 14 10003 757 Mill Valley … 4.0 12000 0 -92.411 40.23 North Ridge 47 80000 5 10004 30001 Hwy 97….. 3.5 8000 4000 -90.012 41.754 Springfield 31 82000 24 GIS Solutions experts work with college staff to identify desired student types used create “Profiles” which will be used to find additional high-probability recruitment areas and addresses. Profile Type 1 PRIZM Segments 45,46,53 Income Range 45000 - 68000 High School ACT Test Scores 24-28 % Minority 50 etc Profile Type 2 PRIZM Segments 24,55,56 Income Range 75000- 95100 High School ACT Test Scores 28-32 % Minority 12 etc Profile Type 3 PRIZM Segments 17,20,31 Income Range 85000- 99000 High School ACT Test Scores 29-34 % Minority 15 etc
16. Student Recruiter by GIS Solutions Inc Use Profile to find new recruitment areas Existing “hot spots” for recruiting. Profile Type 1 PRIZM Segments 45,46,53 Income Range 45000 - 68000 High School ACT Test Scores 24-28 % Minority 50 etc Profile Type 2 PRIZM Segments 24,55,56 Income Range 75000- 95100 High School ACT Test Scores 28-32 % Minority 12 etc Profile Type 3 PRIZM Segments 17,20,31 Income Range 85000- 99000 High School ACT Test Scores 29-34 % Minority 15 etc Apply GIS Solutions technology to locate new recruitment areas based in desired student profile types. New high-probability areas for recruitment
17.
18. Student Recruiter by GIS Solutions Inc Map Details Maps Downloads Tables Student Recruiter Show on Map Target Zones Target High Schools Target Addresses Current Students Alumni Admissions Counselors School Details Current Student Data Address 377 Hill GPA 3.7 HH Income 84,000 Tuition Paid 8,200 School Based Financial Aid 3,800 etc. Recruitment Target Location Address 201 Oak ….. PRIZM Segment 42 HH Income 87,000 Ethnicity Hispanic etc. Alumni Address 100 Cherry... Name Joe Smith HH Income 101,000 Year Grad. 1978 PRIZM 42 Show on Map Target Zones (High School Boundaries) Target High Schools Target Addresses Current Students Alumni Admissions Counselors
19. Maps Downloads Tables Student Recruiter Student Recruiter by GIS Solutions Inc Table View – Target Schools Target Recruiting Schools Target Recruitment Addresses The system produces a table of high schools which have characteristics similar to the recruitment profiles defined previously. School Name Address Contact Match Confidence ACT SAT Verbal & Math PSAE Average Household Income % minority etc. Eastside 100 Oak St.. Mr. Bob Jones … 95% 28 1210 95 65000 29% Westside 200 Cherry Mrs. Mary Wilson …. 89% 30 1150 87 29000 2% Southside 300 Magnol.. Mr. Frank Norris … 92% 31 1204 91 47000 12% North Ridge 100 Oak St.. Mr. Harold Smith…. 95% 28 1210 95 65000 29% Benson 200 Cherry Mr. Fred Johnson … 89% 30 1305 87 29000 2% Hamilton 300 Hillside Mr. Ben Olson .. 92% 31 1195 91 47000 12% Newbury 702 Mountain Mr. Sam Jones… 92% 31 1320 91 47000 12% etc
20. Maps Downloads Tables Student Recruiter Student Recruiter by GIS Solutions Inc Table View Target Addresses Target Recruiting Schools Target Recruitment Addresses The system produces a table of addresses which have characteristics similar to the recruitment profiles defined previously. Address PRIZM Segment Match Confidence Household Income Ethnicity etc. 100 Oak St.. 42 95% 120000 Hispanic 200 Cherry Rd … 37 88% 78000 Black 300 Magnolia Ave .. 25 88% 100500 Black 100 Oak Street …. 41 99% 94000 White 200 Cherry 57 89% 88000 Asian 300 Sherwood St…. 33 92% 88000 Pacific 300 Michener….. 29 94% 89000 Native American etc
24. Maps Downloads Tables Student Recruiter Student Recruiter by GIS Solutions Inc Download Maps and Tables Recruiting Maps as PDF Target High Schools as Excel File Target Addresses as Excel File Etc. Download Files are downloaded to local computer where they can be used to support recruitment efforts or loaded into other software systems.
25. Student Recruiter by GIS Solutions Inc Efficiently build your profitable student population Increase: 1. Efficiency 2. Enrollment 3. Tuition 4. Diversity 5. Profitability Results
26. GIS Solutions Inc 2612 Farragut Drive Springfield, IL 62704 217.546.3635 www.gis-solutions.com Student Recruiter by GIS Solutions Inc Contact Information Tim Johnson, President [email_address] 217.836.3639 Ron Skupien, Marketing and Sales [email_address] 312.303.648