This chapter explores the relationship between active learning strategies and skills and attributes that enhance learning (SAEL) among college students. Developing skills and attributes that enhance learning (SAEL) among college students is critical to student success and persistence in college. Additionally, SAEL help the students develop a sustained learning commitment while in college and after graduation. However, little evidence is there to show how higher education institutions are equipping students with SAEL. This study seeks to investigate if there is a relationship between active learning strategies (ALS) and SAEL. Secondary data from the 2007 National Survey of Student Engagement (NSSE) at a Midwestern state university in the USA were employed to examine the relationship between ALS and SAEL. The results of the analysis showed positive significant correlations between ALS and SAEL components, (p < 0.001). Multiple regression model showed that ALS predictor variables significantly predict SAEL, R2 = .196, R2adj = .188, F (7, 731) = 25.38, p < .001. The regression model accounts for 19.6% of variance in SAEL.
This study used hierarchical linear modeling (HLM) approach to investigate relationships between student achievement and single-sex school status with a sample of 57,041 students in 996 secondary schools in Kenya. An ANOVA was conducted to compare achievement levels of student enrolled in computer science courses and those who are not. The results showed that students enrolled in computer science courses achieved at a higher level whether in single-sex or coeducational schools. Students in single-sex schools achieved at a significantly higher level than those in co-education schools across all counties studied and across all subjects. The study concluded with a discussion of the importance of the study findings and call for the education stakeholders to be cognizant of the contribution the variables discussed in this study make to teaching and learning environment so that they are fully involved in providing the kinds of educational experiences that promote student learning.
This study used hierarchical linear modeling (HLM) approach to investigate the impact of school leadership, professional development, gender and teaching experience on professional commitment with a sample of 396 elementary and secondary school teachers and administrators from Kenya. The HLM results indicate significant differences among schools (?2 (49) = 218.92, p<0.001), with an intra-class correlation of 0.3183 indicating that 31.83% of variance in professional commitment was among schools. When professional development and school leadership were used as level 2 predictor variables with no level 1 predictors, the school variability dropped from 4.73584 to 3.30865 indicating that 30.14% of variance in school professional commitment was due to school leadership and professional development (?2 (47) = 161.67, p<0.001). Further, the reliability of the sample means in any school for the true mean school professional commitment was 0.703.
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.
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