An Analysis of First Year Undergraduate Student Attrition at the University of Adelaide R. SENAKA ARACHCHI

Transcription

1 An Analysis of First Year Undergraduate Student Attrition at the University of Adelaide R. SENAKA ARACHCHI

2 Outline 1) Introduction 2) Purpose of Study 3) Data Collection Method & Analysis 4) Results 5) Conclusions University of Adelaide 2

3 Definition of Attrition Attrition is defined as the percentage of a commencing cohort that has not completed and subsequently not continues in the next year. University of Adelaide 3

4 Introduction 1) Why Attrition Important Financial Losses Lower Reputation 1) Why Renewed Attention for Attrition Funding Cuts Intense competitions Diversity Among Students 3) Current Level of Attrition Wide Variations Low among Go8 Highest at Adelaide University of Adelaide 4

5 Attrition Rate for all domestic commencing bachelor students Go8 (normal calculation) Sydney UNSW Monash Melbourne UQ UWA Adelaide ANU Sector Source: DET Higher Education Statistics 2014 University of Adelaide 5

6 Attrition Rate for Domestic Commencing Bachelor Students (Go8) Sydney UNSW Monash Melbourne UQ UWA Adelaide ANU Sector University of Adelaide 6

7 Purpose of the Study 1. Identify most significant variables related to attrition of first year UG. 2. Supplement information for an on going project implemented to improve retention of UG. 3. Provide inputs to formulate viable policy options. University of Adelaide 7

8 Data Collection Method & Analysis Commencing UG students: Domestic and International 1) Data retrieved from enrolment records ) Match student id to find continuation 3) Flag attrition status for Commencing UG: Yes No 4) Included variables for analysis based on literature and availability University of Adelaide 8

9 Number of Commencing Undergraduate Students Included in the Analysis and their Attrition Rates Residency Status Domestic Attrition Rate 20.8% 21.0% 22.9% 22.1% 22.0% 21.8% Internation al Attrition Rate 6.7% 7.8% 7.5% 8.8% 8.2% 7.7% Total Attrition Rate 18.1% 18.4% 20.0% 20.0% 20.2% 19.4% University of Adelaide 9

10 Variables For Analysis 1) Demographic & Social Age, Gender, ATSI status, Residency, Disability, NESB, Low SES, Remoteness, 2) Academic and Program Related Basis of Admission, ATAR, Program(FoE), EFTSL,GPA, Type of Attendance University of Adelaide 10

11 Data Analysis Univariate Analysis 1. Chi-square tests for categorical variables 2. Univariate Logistic Regression for continuous variables Multivariate Analysis 1. Multivariate Logistic Regression (for both categorical and continuous variables) University of Adelaide 11

12 Chi-square test: Association with Attrition: Comparison_ Domestic and International Variable Domestic Attrition International Attrition Gender + High (Female) NS Disability NS NS Secondary qualification + Low + High H Ed Qualification NS + Low Society and Culture + High + High Natural & Physical Sciences NS + High Management & Commerce + Low + Low Information Technology NS NS Health NS + Low Engineering and Related + Low + Low Education + High NS Agriculture, Environmental + High + High Architecture and Building + High NS Creative Arts NS + High Full Time Attendance + Low + Low University of Adelaide 12

13 Order of Variable Selection for Logistic Regression for Domestic Students Step GPA. GPA. GPA. GPA. GPA. GPA. 2 Broad FoE EFTSL. EFTSL. EFTSL. EFTSL. Broad FoE 3 EFTSL Gender. Gender. Broad FoE 4 Gender Remote- Broad FoE Remote- Regional Regional. 5 Basis of Disability. Remote- Gender. Admission 6 Remote- Regional Broad FoE 7 Disability SES Postcode Basis of Admission Regional Disability. Basis of Admissio n Fair way Instituti on 8 NESB NESB. Broad FoE SES postcode Basis of Admission NESB Gender EFTSL. Basis of Admission Gender Disability Full Time/Part Time 9 ATAR 10 Full Time/Part Time 11 SES Postcode University of Adelaide 13

14 The analysis of logistic regression results for domestic students Less likely to be withdrawn compared to opposite groups Male NESB Disabled Students from remote and regional areas More likely to be withdrawn compared to opposite groups Low SES Low ATAR low GPA low Unit Load Part-time Attendance University of Adelaide 14

15 Conclusion The most significant predictor of attrition is low GPA. Part time attendance and small number of study units enrolment and ATAR are significant predictors. Gender, NESB, Low SES, Disability are significant predictors. Student admitted based on mature age and TAFE qualification have a higher attrition rates but are insignificant predictors of attrition. More students have withdrawn from architecture and building, Agriculture fields as against Engineering which demand strict entry requirements with strong academic standing University of Adelaide 15

16 Conclusion Evidence does not support that attrition is always positively associated with disadvantaged students (E.g. Low attrition among NESB, Remote/Regional students) Females are more likely to withdraw compared to males. High achievers are more like to continue than poor achievers. ATAR scores indicate inverse relationship with attrition. Age and ATSI status have positive relationship with attrition but not significant predictors. University of Adelaide 16

17 Conclusion The first term academic results are critical for student attrition. It is important to pay an immediate attention to identify and help non performing students. Poor academic performance need to be identified early as GPA is found to be an important predictor of attrition. University of Adelaide 17

18 Conclusion Administrative data can be effectively used for identification of students at risk of failure i.e. students from equity groups: Low SES students. The most significant student information relating to attrition could be used for early intervention in the form of providing maximum support for students more likely to withdraw early. University of Adelaide 18

19 Conclusion Some factors associated with student attrition are beyond the control of higher education institutions, they can certainly improve the retention by being responsive to issues that are within their control. University of Adelaide 19

20 Study Limitations Analysis is based on secondary Information. Need to have qualitative information with particularly references to personal circumstances for attrition. Weakness of some indicators such as socio-economic status, remoteness as measurements of disadvantaged students. University of Adelaide 20