We're happy to announce a new feature in BbStats: Latent Class Analysis with Annual adoption report.
Some time ago we reported about a new study at the University of Illinois at Chicago about Patterns in Faculty Learning Management System Use . This research was featured at the DevCon in 2017 and in Top 7 findings in the new study on Blackboard usage .
Running your own Latent Class Analysis study may be of interest to you, but likely many priorities are competing for your time. By the way, Latent Class Analysis is a statistical method for identifying unmeasured class membership among subjects using categorical and/or continuous observed variables. So, while we can't run a custom study for you, it is now possible to identify latent groups already documented. BbStats will graph out how many of your courses belong to each latent group. It doesn't do that by running the model itself, you would do that in order to discover new latent groups or groups unique to your organization. So, as long as you accept the findings of the UIC study, BbStats will identify three documented groups in your data.
Latent group analysis is a process of grouping data to discover new patterns. In a way, the data itself speaks to you through the emerging patterns. Extracting the course design data from Blackboard with BbStats and running the statistical model at UIC identified the following groups: Holistic, Complementary, and Content Repository.
Course data was extracted from 2562 courses with 98,381 student enrollments during the Fall of 2016. A latent class analysis was conducted to identify the patterns of LMS tool use based on the presence of grade center columns, announcements, assignments, discussion boards, and assessments within each course. Three latent classes of courses were identified and characterized as Holistic tool use (28% of the courses), Complementary tool use (51%), and Content repository (21%).
Following is the process to replicate the study report in your Blackboard Learn system:
1. Count all courses with an active grade center. According to the study, this includes Holistic and Complementary courses, but excludes Content Repository courses.
2. Count all courses with an active grade center, announcements, and discussion forums, which identify the Holistic group, with very close approximation.
3. Identify all courses accessed by students, which indicate courses that were active at some point in history.
With the above three groups identified, the system makes the following calculations:
Holistic courses = All courses with an active grade center, announcements, and discussion forums.
Complementary courses = All courses with an active grade center minus Holistic courses
Content Repository = All courses accessed by students minus all courses with an active grade center
This figure, taken from the UIC study, shows the basis for the above calculations:
There are two ways of running the Latent Class Analysis report in BbStats.
1. You can specify a pattern for the course_id to investigate course groups or departments. This may be different for your school (Figure 2). Examples:
Show latent groups for the Fall 2018 courses for the Computer Information Systems department:
Show latent groups for calendar year 2016:
Identify latent group for a specific course:
2. The second way of using the new report is to run the Annual Latent Class Analysis. This will show academic year breakdown based on course creation date from March 1, 2013 to March 1, 2018. The assumption is that courses are created prior to the Spring/Summer term after March 1. This report does not require any course_id patterns (Figure 3).
Try it in your staging system. OCELOT: BbStats.
The system expects that courses are created inactive and instructors enable courses for students to see. This means that if students access courses, these courses are active. Also, any new gradebook column counts as usage of the gradebook. So, if all new courses are pre-populated with gradebook columns, the complementary category will be very high. However, if faculty create their own gradebook columns by copying courses and if inactive courses are not available to students, the graphs should be accurate.