What is In-Person

The authors of this study utilized the logistic regression analysis using extreme student groups (top and bottom quartiles) defined by students' collaborative learning scores to develop a model for predicting group membership of low and high levels of collaborative learning college students. The focus of the study was to identify characteristics of the learning environment that differentiate between high and low collaborative learning groups. Results of the logistic regression showed a statistically significant model that can be used to reliably predict student's classification into low or high collaborative learning groups based on the selected institution and personal variables. The logistic regression model showed the lowest total percent correctly classified was at 98.1% while the highest total percent correctly classified was at 98.6%. Majority of the model variables made significant differences between the low and high collaborative learning groups. ANOVA results indicate significant group differences in all the predictor variables.