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Of 18 students originally, I had 16 finish the course.
Excluding the 2 who dropped from the data, the normalized
gain on the FCI using class averages of the tests was 0.57
I had 1 student who made a 30 on the pre-test, but missed 1
on the post-test (and he knew which question when I told
him), but the normalized gain would have a 0 in the denom.
Excluding him, the average of the individual gains was 0.62
with s=0.19.
The smallest gain was 0.32 from my worst student (went from 5
correct to 13 correct). The largest was 0.96 ( 7 correct to
29 correct), but he's still poor at actually solving problems.
The average score went from 14.0 (s=7.4) to 23.2 (s=5.3)
excluding the 2 drops. Of the 16, 8 scored <20 on the
pre-test and only 2 were <20 on the post.
I taught the course using a SCALE-UP model with lots of
peer-interaction and in-class experiments.
I think I can safely say that there was an improvement in
concepts. For the better students, the improvement was more dramatic.
Unfortunately, I only have 1 FCI data set data from when I
taught the class traditionally. In that I had 13 students
who finished. The gain based on average scores was 0.65, and
the average of the individual gains was 0.65 (s=.16). 10
students <20 on the pre-test and only 1<20 on the post.
While there's not a big difference in these two data sets,
the students in the interactive setting were much more alert
and enjoyed the class more.
I should add that most of these students were engineering majors.
I didn't have time to administer the Lawson test (Scientific
Knowledge)