Competency-based Institutional Learning

Competency-based Education (CBE) has been promoted as a solution to controlling costs, closing the gap between what colleges provide and employers want, and improving graduation. One element not often cited, but essential for meeting these promises, is the collection and use of learning data to improve learning and progression toward graduation.

What data needs to be collected and how is it used?

Depending on one’s systems, there’s a wide variety of faculty and student activity data (which should be collected to support regular and substantive interaction), demographics, transfer, and learning assessment data, both formative and summative. These data may be used directly to identify barrier courses, chapters, modules, even questions. The key is to set up people, processes, and reporting to give the data experience designers and others need.

CBE is particularly rich in learning data due to the extensive assessment required by the model. Not only are competencies evaluated and collected, but often sub-competencies, knowledge, meta-cognitive, and non-cognitive information is also collected.

Learner reports can allow accountable counselors, mentors, and teaching faculty to monitor and intervene on a student’s behalf when they are struggling. Learner reports can be aggregated to determine areas of strength and weakness in the learning of a particular class. Value can be added to this data by setting thresholds for risk that prioritize student interactions for a group, or prioritize learning objectives for deeper instruction.

Much harder, but potentially more valuable, is when learning analytics is applied to the data to discover what correlations and predictions emerge that were difficult to see. One institution, for example, found that not GPA, but the specific grade in Psychology predicted success in the program of interest. This led to further research and understanding of the interest level, knowledge, and other items missing from areas of the curriculum.

Setting up analytics may be difficult for a smaller school because of the technical expertise involved. It may be beneficial to investigate some of the platforms with basic analytics built in, such as NuroRetention and Moodle. And send some faculty and IT staff to training on the subject to ensure your institution is keeping up with the fast-moving evolution in the field. WGU has a new master’s program in data analytics.

In the end, as an institution develops its capacity to measure, interpret, analyze, and act on evidence, it becomes a stronger, more effective institution. CBE is a natural fit to this model, but any institution may benefit. Once in place, intentional experimentation and theory building about the institution’s model are possible.

Disclosure: From time to time I’m employed by NuroRetention to help explain their product. I like their product so well that I also volunteer my time to advise them on features.