Applying Cognitive Load Theory to the Design of Online Learning. Page: 39
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by using a cluster sampling method. Six groups were involved in the study, with a total
sample size of 146 individuals.
Independent samples t tests with an alpha level of .05 were used, assuming a
large effect size and a statistical power level of .80. These tests were used to compare
the mean performance scores of the treatment groups (i.e., the sections using
redesigned materials) versus the control groups for all three courses. Cohen's d was
also used to measure effect size. Overall cognitive load scores were similarly compared
for each comparison group, for a total of six data points in the study. SPSS 12.0
statistical analysis software was used for all analyses.
In comparisons made on the effects of split-attention and redundancy, no
statistically significant difference was found between the test groups. Significance was
found between the experimental and control group on the effect of modality. On the
three measures of cognitive load, no statistically significant differences were found.
Conclusions
Hi: There is no statistically significant difference in learning scores between students in
the class using materials redesigned accorded to the split-attention effect of cognitive
load theory (CLT) and those in the control group.
Prior to the study, I believed that eliminating the students' need to search through
several noncontiguous sources of information would improve outcomes. However, the
study found no statistically significant difference (p = .684, p>.05) between the groups. It
could be concluded from this section of the study that, in this case, redesigning online
learning to reduce split attention held no relationship to performance on outcome
measures and yielded little practical basis (d = .114) upon which to reduce or eliminate39
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Burkes, Kate M. Erland. Applying Cognitive Load Theory to the Design of Online Learning., dissertation, May 2007; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc3698/m1/47/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .