Internet Learning Volume 3, Number 1, Spring 2014 | Page 76

Effect of Student Readiness on Student Success in Online Courses lege courses. All three instructors had over seven years of online teaching experience each teaching online and had established student engagement skills. Additionally, all three had successfully completed at least two QM professional development trainings, were certified QM Master Reviewers, and had been active participants in the Middle States accreditation process. Participants The data collected in this study occurred in 11 classes with a total of 200 students over the period of two semesters – Fall 2012 and Spring 2013. Students were required to take the SmarterMeasure Learning Readiness Indicator before beginning the course work. The Indicator is a vetted web-based tool which assesses a learner’s likelihood for succeeding in an online and/or technology-rich learning program by measuring the degree to which an individual student possesses attributes, skills, and knowledge that contribute to success. At the end of the semester, a correlational analysis was run to measure the relationships between SmarterMeasure scores and measures of course retention and grade distribution as measures of academic success. The study was conducted for two semesters to ensure a valid data pool. SmarterMeasure data for six indicators were aggregated based on a percentage scale of 0% to 100%. The six indicators include On-screen Reading Rate and Recall; Typing Speed and Accuracy; Life Factors; Technical Knowledge; Reading Comprehension; and Individual Attributes (including motivation, procrastination, and willingness to ask for help). The final grades earned for the selected CSM courses was aggregated and rated by academic success. The findings were analyzed through Chi square tests for statistical significance. At the end of the semesters, we conducted a statistical analysis to measure the relationships between SmarterMeasure scores and CSM measures of course retention and grade distribution as measures of academic success. Statistical Analysis A Chi squared analysis was conducted to search for statistical significance to the scores of the SmarterMeasure assessment compared to the final course grades the students earned in the selected course sections. The six SmarterMeasure indicators scores were aggregated and compared to the final grade the individual student earned in the course. SmarterMeasure scores rely on student answers, some being subjective (life factors and individual attributes) as well as objective measures. The scores from the SmarterMeasure assessment are delivered as ranges being labeled blue for rates between 85% and 100%; labeled green for rates between 70% and 84%; and labeled red for rates between 0% and 69%. As we analyzed the data, we realized that (a) there were a number of small cells, and (c) there were “zero” cells. Therefore, as per acceptable social statistical analysis, the only practical alternative was to combine categories in such a manner as to eliminate these small and zero cells. The red cells were highly problematic in most of the cases; therefore, we combined the green and red labels (frequencies) to eliminate any biasing that the low red frequencies may have introduced into the analysis. Therefore, we used two SmarterMeasure Indicator Rates – (a) students earning a rate from 85% to 100% (the blue labels), and (b) students earning a rate from 0% to 84% (the green and red labels, combined). The final grades for the class were measured as “successful” at the rate of 70% 75