The most useful takeaway from this module was the shift from lagging, negative indicators (attrition, failing grades, post counts) to predictive, positive indicators built through regression analysis on specific instructor behaviors tied to specific curricular components. Generic averages and "best practice" thresholds often miss the mark — the 2009 US Department of Education meta-analysis confirmed that none of the general best practices showed significant evidence of contribution to student success.
What stood out most was the principle that each class has its own roadmap. Behavioral profiles built from course-specific statistics are far more useful than generalized expectations applied uniformly across instructors and weeks.
I also valued the reminder that corrective action shouldn't focus on the instructor alone. Curriculum materials and technology applications must be part of the analysis, since high performance in one section often points to a particular approach or tool worth replicating.
Application: At the CVCC Amherst Early College Center, I want to think of quantitative thresholds as the "ground floor" — a starting point — and shift more attention to the qualitative dimensions of instructor-student interaction once basic activity levels are met. I also plan to look more carefully at curriculum and technology when reviewing performance, rather than defaulting to instructor-focused corrections.
With Benevolence, Shannon
