Informatics Support for an Online Scalable Diagnostic ...

Informatics Support for an Online Scalable Diagnostic and Prescriptive Student Retention System for Health Science Community Colleges Suvendra Vijayan1, Ronald Johnson DDS2, Craig W. Johnson PhD3 The University of Texas School of Public Health, Houston TX 2 UTHealth Office of Cultural and Institutional Diversity, Houston, TX 3 The University of Texas School of Biomedical Informatics, Houston TX 1

Abstract An online scalable system electronically collected and stored student diagnostic screening survey data, student risks for academic success, end-of-semester student retention status, and implemented secure transmission of reports to program coordinators for 512 new 1st-year students enrolled for fall 2010 and spring 2011 semesters in 17 programs within a community college for health sciences. Documented predictive validity domains for the Personal Background and Preparation Survey were shown to extend to Health Science Community College students. Introduction The recent median and mean first-year to second-year student retention rate reported by community colleges was 56% and the median degree completion rate reported was 23%1.The present study was conducted to assess baselineyear predictive validity and scalability of a system of online tools for student retention in a community college for health sciences preceding intervention-year implementation, in which students it identified as high risk for adverse academic status events (AASE), were to be assigned to targeted retention interventions. Research questions addressed the degree to which the online Personal Background Preparation Survey (PBPS) total risk score, its new risk self-assessment subscale score, and underrepresented minority student (URMS) status predicted AASE. Methodologies Disparate commonly available online tools were used to collect and store data because of institutional constraints and the variety of data sources. Availability and familiarity with these online tools facilitated program execution. New 1st-year students (N = 512), enrolled in 17 programs within a community college for health sciences fall 2010 and spring 2011 semesters, responded to the PBPS via Lime survey (www.limesurvey.org) after receiving E-mail invitations. To help secure confidentiality, invites included links to URLs having individualized unique computergenerated tokens. Data was downloaded as a CSV file to a SQL database maintained within the UTHealth system. A Microsoft Access-based frontend to the database generated PBPS-based reports including individualized confidential student and diagnostic/prescriptive advisor reports and comprehensive institutional reports. Reports were transmitted securely via X-files to the health science community college Program Coordinator. The college generated reports of student AASE every semester and at yearend using Excel templates transmitted by X-files. The Excel templates included AASE taxonomic coding definitions of 27 categories of student outcomes (e.g., SW – spring warning, SP – suspension, GS – good standing). Access then linked and queried the Excel and CSV files to produce an integrated file containing all independent and dependent variables including AASE. Data was screened for missing or duplicated entries before analysis. Final predictive validity statistical analyses used SPSS logistic regression with PBPS total risk score, risk self-assessment score, and URMS status as predictors of AASE, Email occupied a central role for communication among program staff and with the students. Results and Conclusion Baseline results indicated that PBPS total risk score, risk self-assessment score, and URMS status were each independent significant (p < .05) predictors of 1st-semester health science community college student adverse academic status events. URMS one standard deviation above the mean PBPS risk score and one standard deviation below the mean risk self-assessment score had approximately triple the AASE odds of nonURMS at the PBPS and risk self-assessment means. Results expand the domains of documented predictive validity of the PBPS to health science community colleges. Taken together with previous results, among health science professional and graduate students, the new online system is expected to provide validated proactive diagnostic and prescriptive tools and strategies for increasing postsecondary health professions educational institution student retention2. Funded by: Texas Higher Education Coordinating Board Minority Health Research and Education Grant Program References (1) Habley W, Valiga M, McClanahan R, Burkum K. What Works in Student Retention? ACT Inc, Community Colleges Report 2010. (2) Johnson C, Johnson R, Mckee J, Kim M. Using the personal background preparation survey to identify health science professions students at risk for adverse academic events. Adv Health Sci Educ Theory Pract 2009;14(5):739752.

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