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%
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