To corroborate self-report data from instructors, the instructor and his or her students answered an identical question about the instructor's use of active learning (Supplemental Material 3, question 8; Supplemental Material 4, question 3).
The workshop introduced a method of cohort analysis with educational data that instructors collect in their teaching-learning environment.
The fact that our data include instructors' analyses of three distinct lessons suggests that the components that were not video-specific are generalizable across active-learning instruction.
The overall objective of both the workshops was to introduce iSAT as a tool for analyzing the data that instructors deal with in their classroom activities.
Unfortunately this result was not replicated in the data for instructor 1, and so there is still insufficient evidence to suggest that pedagogy can be tailored towards those of a particular orientation at the expense of another.
DataPad was founded in 2013 by Wes McKinney, who wrote "Python for Data Analysis" (believe me, it's apparently a big deal among Python programmers) and creator of Pandas, and Chang She, who was a core developer of Pandas and a former data science instructor at Columbia University.
Most data support instructor-formed groups (cf. [28]) and any form of group diversity, including academic ability, class, gender, and ethnicity, increases cognitive disequilibrium that helps achieve deeper learning [17].
While the students collect data, the instructor would ask one group why either of the basic models developed so far in the class (prokaryotic or plant cell model) do not apply to this current cell phenomenon.
The data from these instructors are included on Figures 1 5 as possible targets for instructors who desire to achieve gains in student achievement.
Again, additional themes emerged from the data, including fostering instructors' connections with the field, and leveraging institutional support for instructor learning.
