These results are valid for our condition set of instructor-mediated classroom and medium complexity programming topic in lecture setting.
To test if our results extend to topics of high complexity under the same condition set of instructor-mediated classrooms, further experiments need to be conducted.
Using an open source anchored discussion system, we designed a set of instructor-based and peer-oriented attention guidance functionalities involving dynamic manipulation of text font size similar to tag clouds.
Finally, we coded 11 videos independently, establishing an average Cohen's kappa of 0.908 ± 0.045 for the set of student codes and an average Cohen's kappa of 0.852 ± 0.069 for the set of instructor codes.
We then independently coded six of our videos and established an average Cohen's kappa score of 0.868 ± 0.084 for the set of student codes and an average Cohen's kappa score of 0.827 ± 0.072 for the set of instructor codes using all pairs of observers.
For example, for three separate case studies that increased in sophistication, students were asked to: 1) propose original hypotheses to explain published observations; 2) self-evaluate and peer-evaluate hypotheses using the same set of instructor-provided questions; and 3) revise to improve their hypotheses.
For instance, our study shows that in a field setting of instructor-mediated college classrooms, active learning with visualization leads to increased learning outcome in terms of behavioral engagement and affect and perception of learning when compared to the lecture method, if the topic is of medium complexity.
If you are writing according to a style manual that differs from the material presented above or for an instructor who wants you to follow a different set of instructions, follow the style manual or instructor's preferences.
A complete set of instructions including software on how remotely conducted laboratory experiments may be set up, is available to motivate other instructors to create additional Internet-assisted exercises.
Next, the four coders divided the remaining instructors and applied an initial set of codes based on instructor strategy items on StRIP survey (Table 2).
