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Second, a t test did not reveal significant differences in pretest scores between the students we dropped from the study (M = 1.43, SD = 0.87) and those we retained (M = 1.85, SD = 1.20), t(151) = −1.89, p = 0.061.
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As it was expected that motivation would vary greatly not only between schools and majors, but also between individual students, we conclude that the data is satisfactory for continued analysis.
Concerning the average measure of similarity (r) between the students' performances and the teachers' models, we observe a value of 0.69 (SD = 0.18).
Since ANOVA showed differences between the Dutch and Swedish students we stratified our analyses by country.
It is important to note that, even though the control courses were selected randomly and there were no apparent differences between the groups of students taking them, we cannot rule out pre-existing differences between the students enrolled in these courses unrelated to PBL, such as age, general background knowledge, or academic ability.
In contrast to the similarity between male and female students, we found significant, if varying, effects of student academic level on performance and improvement.
Their simple nature allows vigorous discussions between students working in groups and between the students and the class facilitator.
Moving to the relationship between the student behaviors and the success components, we note several interesting findings.
Dwivedi recalls the deep connection that typically develops between the visiting students: "About three days before we were all to depart, all of us almost simultaneously broke down and started crying because we were going to miss each other so much.
We see a direct link between the work we do and supporting students who are leading this struggle".
Statistical differences were tested using ANOVA followed by Tukey-Kramer multiple comparison test and for comparison between the means we used a t-student test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com