In an adaptive learning system like McGraw-Hill’s ALEKS, students learn material targeted to their specific needs and progress at their own pace. Thanks to the adaptive nature of the technology, it is not generally a concern whether a student is a fast or slow learner, or starts learning from a stronger or weaker foundation, because the system adjusts to the student. But some students may still struggle, and can benefit from the intervention of an instructor.
What if a student engages in cramming behavior? What if a student, who until now was progressing at her own steady pace, starts showing signs of struggling? What if there are changes in the student’s behavior that are symptomatic of possible issues that require attention from the instructor? Ideally, we’d want instructors to be alerted to those situations so that they can intervene and connect with the student before it might be too late.
This summer, we are launching a new tool called ALEKS Insights that leverages machine learning and the data in ALEKS to help solve these problems for instructors. How did we create it? This post explains the research  we did here at McGraw-Hill that led to its development.
To design a system that could alert instructors about struggling students, we began by researching and mapping out the various learning patterns shown by anonymous students in the ALEKS system. To get a sense of how students were working in ALEKS, we relied on several statistics that described the learning activity, such as:
- problems learned per hour of login time
- rate of access to explanations
- rate of correct answers
- problems learned per day in course
More precisely, we looked at how these statistics evolved for each anonymous student throughout their time in ALEKS. From this, several different types of learning patterns emerged. For example, some students started out making good progress, as shown by a relatively high rate of problems learned per hour. However, after a certain amount of time, the rate of learning would begin to slow, eventually dropping to a low level. Here’s an example of such a profile: