Interpreting Crisis of Attrition and Social Isolation in Undergraduate Degree Programs using Asynchronous Learning System

This study evaluates hidden crisis of attrition prevalent amongst students and programs with descriptive interpretation for solving social isolation. A Descriptive research design was adopted for this study with a variable values covering three (3) undergraduate degree programs. A Simple Percentage Method, chi-square tests, Tables and weighted average were used to get clear picture of analysis, of the academic and social characteristics of newly admitted undergraduate degree seeking students (N = 45) in the College of Business and Technology at Parker University, Dallas, Texas, United States from Fall 2013 to Fall 2014. A binary logistic regression analysis is proposed and performed to predict the probability of a student dropping out. A principal component factor analysis with descriptive analysis was performed on the twelve (12) questionnaire items used for data analysis. Student outcome (persistence or dropout) was the criterion variable. Analysis was not conducted to raise the assumptions of (adequacy of sample size, presence of outliers, factorability, linearity, and multi collinearity) because of the low number sample size (N=45). However, the general structure matrix pattern was examined for item loadings of three (3) programs in the undergraduate degree plan, in order to determine the number of factors to retain for convergent validity. Methods include Eigen functions, eigen values greater than one rule, scree test and total variance utilized to interpret factor structure coherent to the variable values. Mitigating factor analysis includes pertinence of program delivery mode, course design, enrollment number, number of students attending at end of the first year, undergraduate grade point average at time of dropout or completion, admission requirement criteria if any/test scores and number of sections to degree completion or number of courses completed at time of dropout.