Bloom’s revised taxonomy (2001) - students do the lower levels of cognitive work (gaining knowledge and comprehension) asynchronously. Focus is on the higher forms of cognitive work (application, analysis, synthesis, and/or evaluation) in class, where they have the support of their peers and instructor - "Flipped Classroom Approach"
This is one of the most useful approaches I have used in smaller classes, particularly for more challenging topics.
I have used this mostly to teach Graduate students in the PSYC 6013 - Advanced Statistics and Research Methods course the idea of Logistic Regression, which is a follow-up topic to the previous overall topic of Regression.
This has been one of the most successful approaches I have used for this topic, since previously, when I taught it by way of 'rote learning' through slides, they did not grasp the concepts easily.
This method has allowed them to be more critically reflective of the material to actively teach themselves the concepts.
See an example of the approach in my classroom and the results by way of the students' presentation.
Image: Fun name of activity to give students the feel of Mission Impossible, the movie. Tom Cruise completes every mission deemed impossible and this is how I inspire my students to complete a challenging topic. Anything is possible! :)
Image: Students are provided with all the resources on the course shell the week before the lecture is scheduled. They are expected to review the resources and come prepared to work in groups at the start of the lecture to teach me (but really themselves) about Logistic Regression.
Observation: Students become very critical of the material, and appear to be more reflective about what it means and how to explain it.
Advice: I tell them to remember the three W's: What? Why? How?
Four students presented on the main ideas related to Binary Logistic Regression, with references to the course materials provided.
Each student presented a different aspect of the model.
Students also included related aspects to the routine in SPSS to compute a logistic regression Model.
Feedback from students post-presentation based on the material indicated that each of them gained a preliminary understanding of the ideas of logistic regression.