Puente Student English Success, Retention, and Persistence at Gavilan Community College

Similar documents
A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District

Evaluation of Teach For America:

Access Center Assessment Report

Do multi-year scholarships increase retention? Results

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X

Faculty Schedule Preference Survey Results

Learning From the Past with Experiment Databases

Multiple Measures Assessment Project - FAQs

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Creating a Culture of Transfer

Student attrition at a new generation university

Basic Skills Initiative Project Proposal Date Submitted: March 14, Budget Control Number: (if project is continuing)

Upward Bound Program

Rule Learning With Negation: Issues Regarding Effectiveness

VOL. 3, NO. 5, May 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

AGENDA ITEM VI-E October 2005 Page 1 CHAPTER 13. FINANCIAL PLANNING

Student Assessment Policy: Education and Counselling

What is related to student retention in STEM for STEM majors? Abstract:

Race, Class, and the Selective College Experience

The Impact of Formative Assessment and Remedial Teaching on EFL Learners Listening Comprehension N A H I D Z A R E I N A S TA R A N YA S A M I

Assignment 1: Predicting Amazon Review Ratings

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Los Angeles City College Student Equity Plan. Signature Page

Final. Developing Minority Biomedical Research Talent in Psychology: The APA/NIGMS Project

Evaluation of a College Freshman Diversity Research Program

Rule Learning with Negation: Issues Regarding Effectiveness

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming

Mining Association Rules in Student s Assessment Data

OFFICE OF ENROLLMENT MANAGEMENT. Annual Report

NDPC-SD Data Probes Worksheet

Research Design & Analysis Made Easy! Brainstorming Worksheet

Effective Recruitment and Retention Strategies for Underrepresented Minority Students: Perspectives from Dental Students

Australian Journal of Basic and Applied Sciences

10/6/2017 UNDERGRADUATE SUCCESS SCHOLARS PROGRAM. Founded in 1969 as a graduate institution.

Predicting the Performance and Success of Construction Management Graduate Students using GRE Scores

Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study

CS Machine Learning

Early Warning System Implementation Guide

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

The University of North Carolina Strategic Plan Online Survey and Public Forums Executive Summary

The Effects of Class Size on Student Achievement in Higher Education: Applying an Earnings Function. Michael Dillon** E. C.

Lecture 1: Machine Learning Basics

School Size and the Quality of Teaching and Learning

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees

Python Machine Learning

Cypress College STEM² Program Application

Supplemental Focus Guide

SERVICE-LEARNING Annual Report July 30, 2004 Kara Hartmann, Service-Learning Coordinator Page 1 of 5

ROA Technical Report. Jaap Dronkers ROA-TR-2014/1. Research Centre for Education and the Labour Market ROA

Segmentation Study of Tulsa Area Higher Education Needs Ages 36+ March Prepared for: Conducted by:

Ryerson University Sociology SOC 483: Advanced Research and Statistics

The Role of Institutional Practices in College Student Persistence

Karim Babayi Nadinloyi a*, Nader Hajloo b, Nasser Sobhi Garamaleki c, Hasan Sadeghi d

Welcome to. ECML/PKDD 2004 Community meeting

Reducing Features to Improve Bug Prediction

Longitudinal Analysis of the Effectiveness of DCPS Teachers

Analyzing the Usage of IT in SMEs

National Collegiate Retention and. Persistence-to-Degree Rates

On-Line Data Analytics

Data Glossary. Summa Cum Laude: the top 2% of each college's distribution of cumulative GPAs for the graduating cohort. Academic Honors (Latin Honors)

College of Education & Social Services (CESS) Advising Plan April 10, 2015

National Collegiate Retention and Persistence to Degree Rates

2017 TEAM LEADER (TL) NORTHERN ARIZONA UNIVERSITY UPWARD BOUND and UPWARD BOUND MATH-SCIENCE

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

THE ROLE OF DECISION TREES IN NATURAL LANGUAGE PROCESSING

CUNY Academic Works. City University of New York (CUNY) Hélène Deacon Dalhousie University. Rebecca Tucker Dalhousie University

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE

Fostering Equity and Student Success in Higher Education

Oklahoma State University Policy and Procedures

Aspiring For More Than Crumbs: The impact of incentives on Girl Scout Internet research response rates

California State University, Los Angeles TRIO Upward Bound & Upward Bound Math/Science

content First Introductory book to cover CAPM First to differentiate expected and required returns First to discuss the intrinsic value of stocks

LaGuardia Community College Retention Committee Report June, 2006

Field Experience Management 2011 Training Guides

Linking Task: Identifying authors and book titles in verbose queries

2012 New England Regional Forum Boston, Massachusetts Wednesday, February 1, More Than a Test: The SAT and SAT Subject Tests

Barstow Community College NON-INSTRUCTIONAL

DISTRICT ASSESSMENT, EVALUATION & REPORTING GUIDELINES AND PROCEDURES

A Diverse Student Body

EXECUTIVE SUMMARY. Online courses for credit recovery in high schools: Effectiveness and promising practices. April 2017

On the Combined Behavior of Autonomous Resource Management Agents

2 nd grade Task 5 Half and Half

Complete the pre-survey before we get started!

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks

ReFresh: Retaining First Year Engineering Students and Retraining for Success

Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for

AGENDA Symposium on the Recruitment and Retention of Diverse Populations

Tablet PCs, Interactive Teaching, and Integrative Advising Promote STEM Success

READY OR NOT? CALIFORNIA'S EARLY ASSESSMENT PROGRAM AND THE TRANSITION TO COLLEGE

Clock Hour Workshop. June 28, Clock Hours

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Procedures for Academic Program Review. Office of Institutional Effectiveness, Academic Planning and Review

The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma

University of Waterloo School of Accountancy. AFM 102: Introductory Management Accounting. Fall Term 2004: Section 4

Decision Making. Unsure about how to decide which sorority to join? Review this presentation to learn more about the mutual selection process!

A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements

learning collegiate assessment]

Transcription:

Puente Student English Success, Retention, and Persistence at Gavilan Community College Terrence Willett Director of Research April 2002 Summary Participation in Puente in general appeared to enhance performance in English classes and progression through the English curriculum. Performance enhancement was more pronounced in English 250, Practical Writing, than in the subsequent course, English 1A, Composition. Introduction Historically, the Puente program had focused on Latino students who intended to transfer to 4- year institutions. Currently, the program focuses on underrepresented students with transfer intent but is open to all. At Gavilan Community College, Puente students are able to enroll in English classes designed specifically for their needs. These classes are based on the 3-fold Puente model of teaching, counseling, and mentoring and have an in-class counselor, ongoing counseling, a mentor component, tours of 4-year colleges, and a motivational conference. The English classes included are a Fall semester section of English 250, Practical Writing, and a Spring semester section of English 1A, Composition. English 250 is required for an Associate s degree at Gavilan College. English 1A is transferable to 4-year colleges and is required for a Bachelor s degree. The goal of these English sections is to help provide a supportive academic setting where Puente students can succeed. This report compares the rates of success, retention, and persistence of Puente students to other Gavilan students in English 250 and 1A. 1

Methods Data on students in English 250 and English 1A from Fall 1994 to Fall 2001 were extracted from the campus MIS database using BrioQuery 6.6. Data from Summer 2001 were not available at the time of this report. Puente English was not offered Fall 2000 or Spring 2001. English 250 had 3,816 enrollments with 182 of those being Puente students and English 1A had 5,884 students with 170 of those being Puente students. Definitions used in this report are as follows: Success rate = proportion of students earning a grade of C or above; Retention rate = proportion of students who did not withdraw; incompletes are defined as not successful but retained; Persistence rate = proportion of students taking English 1A after taking English 250. Those few cases where a student took English 1A before or simultaneously with English 250 were not counted as persisters. Definitions of success and retention are those adopted by the Research and Planning Group of the California Community Colleges. Raw rates were compared between Puente and non-puente students using a Chi-square analysis. Success rates in English 250 and 1A were also predicted using a forward stepwise likelihood ratio logistic regression, a C5.0 machine learning algorithm with 10 fold boosting, and a pruned Classification and Regression Tree (CART) with Gini impurity to ensure that other variables were not confounding the analysis. Predictor variables included Puente participation, GPA, gender, language, high school origin, age, educational status, employment hours, total units attempted, Ethnicity, and educational goal. An analysis of covariance (ANCOVA) provided another test of the effect of Puente on success rates controlling for GPA. SPSS 10.0.7 provided the analysis for the logistic regression and ANCOVA. Clementine 6.6 provided the analysis for the C5.0 and CART algorithms. Chi-square values were calculated manually. 2

Results Compared to non Puente students, Puente students succeeded in significantly higher rates in English 250 and 1A, were retained at significantly higher rates in English 250, and persisted from English 250 to 1A at significantly higher rates when including all students but not when restricted to only those who consistently stated transfer as an educational goal (Table 1, Figure 1, Figure 2, Figure 3). Logistic regression suggested that English 250 success rates could be predicted by Puente participation. For English 250, significant predictor variables in order of entry included GPA, Puente participation, gender, language, and high school origin (Cox and Snell R 2 = 0.375, Nagelkerke R 2 = 0.504, p < 0.005, accuracy = 79.9%). Participation in Puente enhanced the probability of success in English 250. The C5.0 had Puente among the fairly complex rules that predicted success (accuracy = 83.51%). A pruned CART used only GPA as a rule predicting that those with a GPA greater than or equal to 1.835 would be successful (accuracy = 79.00%). The ANCOVA suggested that Puente participation had a significant positive effect on success in English 250 when controlling for GPA (Puente effect F(1, 3642) = 61.453, p < 0.0005; R 2 = 0.378). For the logistic regression predicted success in English 1A, significant predictor variables in order of entry included GPA, educational goal, gender, age, language, Puente participation, and employment hours (Cox and Snell R 2 = 0.298, Nagelkerke R 2 = 0.409, p < 0.005, accuracy = 78.0%). The C5.0 did not have Puente among the fairly complex rules that predicted success (accuracy = 81.08%). A pruned CART also did not use Puente participation as a prediction rule (accuracy = 78.74%). Participation in Puente enhanced the probability of success in English 1A. The ANCOVA confirmed that Puente participation had a significant positive effect on success in English 1A when controlling for GPA (Puente effect F(1, 5758) = 4.445, p = 0.035; R 2 = 0.305). 3

Discussion Participation in Puente appears related to enhanced academic performance in English in general and especially for English 250 where effect sizes were larger. Three of four statistical models found Puente participation increased the probability of success in English 250. For English 1A, the two classical statistical models (logistic regression and ANCOVA) showed a significant relation between Puente participation and enhanced success rates while the two data mining models (C5.0 and CART) did not. Most extraordinary was the difference in persistence rate when including all students. Part of this difference may be due to the Gavilan Associate s degree requirement of English 250, which does not provide incentive to take English 1A. The incentive to take English 1A exists for students who wish to transfer to a 4-year college and who do not constitute a large proportion of non-puente students but does include all Puente students. Comparing transfer oriented non-puente students to Puente students did not show a significant difference in persistence rates but the rates were almost maximally high for both groups. The weaker effects of Puente on the second English class (1A) could be explained by non Puente students who take English 1A having a greater commitment, more institutional involvement, effective social support, and/or more academic experience that simulates the involvement, support, and training obtained through Puente. Other explanations are also possible and could be explored and tested with further research. It must be noted that as students were not randomly assigned to Puente participation conditions, self-selection may play a role in academic performance effects. Statistical methods of controlling for differences in factors such as GPA have been employed but differences due to unmeasured factors such as parental support or parental educational level have not been controlled for. This limitation should be kept in mind when interpreting results or generalizing findings. 4

Table 1. Success, retention, and persistence rates in English classes for Puente and non-puente students. English 250 English 1A Persistence Rate from English 250 to 1A English 250 students All English with 250 Transfer Students Goal Success Rate Retention Rate N Success Rate Retention Rate N not Puente 53% 76% 3634 62% 81% 5714 17% 95% Puente 82% 92% 182 70% 85% 170 70% 99% X 2 59.19 23.49 3.98 1.83 395.18 2.04 df 1 1 1 1 1 1 p <0.005 <0.005 0.046 0.18 <0.005 0.15 Statistically Significant? Yes Yes Yes No Yes No Success rate = proportion of students earning a grade of C or above; Retention rate = proportion of students who did not withdraw; incompletes are defined as not successful but retained; Persistence rate = proportion of students taking English 1A after taking English 250. 5

English 250, Practical Writing not Puente Puente 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Success Rate Retention Rate Figure 1. Success and retention in English 250 by Puente participation 6

English 1A, Composition not Puente Puente 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Success Rate Retention Rate Figure 2. Success and retention in English 1A by Puente participation. 7

not Puente Puente 100% 90% 80% Persistence Rate from English 250 to English 1A 70% 60% 50% 40% 30% 20% 10% 0% All English 250 Students English 250 students with transfer educational goal Figure 3. Persistence from English 250 to English 1A by Puente participation and educational goal.` 8