On this blog and in our library of white papers, I’ve published a number of pieces about how Acrobatiq’s adaptive learning platform transforms learning data into actionable insights about each student’s progress. In this article (and others to come), I’d like to share some practical steps educators can take to improve learning outcomes using that data.
Once adaptive learning software starts generating learning data about a course, educators can extend the impact of that software by using it to make specific interventions. These interventions might be at the level of:
- the classroom by an individual instructor,
- the course by a department’s curriculum committee,
- the department or program considering curricular changes, or
- the administrators who are considering the strategic use of resources.
Classroom interventions using learning data
In the short-term, learning data can be put to immediate practical use by making changes to content and activities while a course is still in progress.
Consider, for example, how many universities are using adaptive learning technology with rolling-enrollment, self-paced academic programs. Because students don’t progress concurrently, it’s important to identify those who may require tutoring or other support. Data from adaptive learning technology provides a clear picture of the progress of each student, showing program staff exactly who needs attention and when.
A more common use case is the flipped classroom format where students work through online courseware outside of class so that the time in a lecture hall or seminar room can be used for active learning to better engage the students.
In that flipped scenario, instructors can use that data about student progress to adjust the plan for classroom time. For example, if the instructor is looking at data about the whole cohort and seeing that most have mastered one concept but not another, then the instructor can revise what material they will discuss in the seminar or what concepts they cover in a lecture.
Another tactic we’ve seen faculty use is to show summary learning data from an online module to their students at the start of a class meeting. They then use that data to explain why they are going to spend the classroom time in a particular way. Faculty in large lecture courses who use this intervention tell us they’ve never seen so many students pay attention because students feel like their activity in the online modules is shaping their learning experience.
Learning data can be used to optimize other familiar teaching practices such as small group work and peer mentoring. Suppose, for example, that the data on the last few practice activities reveals that six students are struggling with a particular learning objective. The faculty member might invite that small group into an office hours session to work on the material together. Alternatively, an instructor can use data to identify a student who understands the material and then connect them with a small group or individual students who haven’t yet mastered it.
Using engagement data for short-term interventions
Even before students begin taking high-stakes assessments, an adaptive learning platform can provide actionable insight in the form of engagement data — information about whether students are simply reading material in a module or actually interacting with the practice activities. Research about online learning shows that engagement with courseware tends to lead to better results, so engagement data is a powerful early signal.
Faculty we’ve worked with have made two very simple changes that have been effective when they see that engagement is low. The first is simply to gate access to the final exam until students have completed certain material in the courseware, thereby improving engagement.
The second is to include on high-stakes exams a selection of questions from the practice activities and to let students know that in advance. We have seen that practice close an engagement gap almost immediately, because it incentivizes students to attempt the practice activities.
Engagement data is also used in practical ways by academic support centers or academic coaches. An advisor typically won’t have domain expertise about a given course, but they do follow students through their career, so they have a sense of when an individual student’s engagement is not where it should be. They can use the engagement data during a single course to see which individual students may need follow up for coaching or other supports during a semester.
Course-level interventions using learning data
An institution can use learning data generated by an adaptive learning platform to make adjustments for large groups of students, both in the short term and to impact later semesters.
Consider, for example, the case of a course with many sections all following the same curriculum. In some instances, data will reveal places where a significant share of students are less successful regardless of the section. In that case, the issue may be with the instructional design.The committee responsible for the curriculum can review the content of the courseware and make adjustments to ensure it is comprehensive and organized in the optimal way for students to learn, often while the course is in progress.
For long-term improvement, the program can analyze data from final exams to see what questions are most influencing the pass rates for the course. Can the D/F/W rate be associated with particular exam questions? Is there a set of questions on exams that most students with failing grades are missing? If so, the data may be illuminating a section of the course where the lecture, reading, or practice activities need to be revised. The course isn’t covering a key concept comprehensively enough or giving enough practice.
While the issue of exam questions needing to be updated isn’t new, today’s course authoring tools makes that process more effective. The traditional approach of adding more supplementary material on top of the assigned textbook to compensate for the gap makes for an unwieldy course design experience for the student. Adaptive learning courseware lets an instructional designer modify and improve the content precisely where it is needed.
Departmental- and institutional-level interventions using learning data
The use of learning data is maturing to the point where it can inform larger strategic decisions for the university. For example, consider a department that is looking at graduation rates for a particular major. Departments are beginning to use learning data to ask questions about the sequence of courses through the major. It’s possible to follow an individual or group of students through that sequence and to develop insights into how the major is designed. Armed with that learning data, the department can make more informed decisions about the curriculum.
Further, deans and provosts should also be able to use learning data in practical ways. In the past, the only data they may have seen was pass rates after a course was completed. They wouldn’t have data that showed why a course might have higher or lower pass rates. And they certainly didn’t get the data in a way that is timely.
Data from an adaptive learning platform lets academic leadership peer a level deeper and identify what part of the course experience is influencing student success. If they can isolate a problem, they can assign an instructional designer to work with the instructors in a targeted way and make a significant amount of improvement quickly.
University leadership can also use the learning data in conversations with faculty about where the resource needs are. In one instance we worked with an institution that was getting very low metrics on engagement data. It turned out they had deployed the software without sufficient attention to design of the courses, which prompted strategic conversations about infusing an instructional design approach into the institution.
Lastly, forward thinking universities are looking at ways to use learning data to inform strategic decisions outside of academic affairs. For example, learning data might reveal that engagement in the first week of the course is significant and that late enrollment is impacting that. A university may use that data to revise activities in the registration or academic advising offices.
Some of these practical uses of learning data are just emerging, but they hold great promise for efforts to improve learning outcomes, improve graduation rates, and close the degree attainment gap.
If you want to learn more about the possible practical applications of the data generated by adaptive learning software, then please be in touch with us to learn more about Acrobatiq’s professional services.