Learning analytics call out for action

Learning analytics is becoming a popular function in learning management. Learning analytics targets a multitude of matters such as tracking course evaluations and students’ progress in their courses as well as even monitoring potential drop-out students. This presentation deals with the monitoring of potential drop-outs. Can we detect them from their LMS data before they actually leave?

The study we report in this presentation is being performed at Häme University of Applied Sciences (HAMK). The target group is the first year students in the Degree Programme in Business Information Technology (Business IT). Annually, the intake is tens of students to both daytime and online studies. We can estimate that about 10 students will leave during or after their first year or later, particularly when they find it difficult to complete their theses. It would be important to learn if there were possibilities for helping the first year students and thus preventing the interruption of their studies.

In Business IT, Moodle LMS is in heavy use. That fact forms the background for the algorithms that we created during this study. The use of Moodle should be constant during the first two years of studies. Moodle is used in every course, and students cannot perform well if they do not use it every day.

An academic year at HAMK is divided into four periods of eight weeks. Each school day is divided into two sections: 8:45-12:00 and 12:45-16:00 with lunch in between. It would be natural to think that the students would log in into Moodle at least twice a day, making the total number of their logins 10 per week per person. But the students do quite a lot of group work and it is possible that they follow their peers’ work in Moodle. It is the school custom that one student returns the group tasks on behalf of the whole group. This could lower the login frequency even if the students are appropriately active in their schoolwork.

For the purposes of this study, we selected a threshold value of four weekly logins. We consider this selection a logical one as we know that if a student logs in into Moodle only 0-3 times a week, there is something wrong. That student would be logging in into Moodle for only 30% of studies or less.

The reduction of a student’s activity level during studies would be detected much later in a traditional environment. In the worst case, a student having dropped out might be detected only the following year when the student failed to enroll for the year. In our study, we monitor the first-year daytime students’ logins into Moodle on a weekly basis.

The study was started by analyzing the data of the first year students in 2014. We were able to construct an algorithm and methods that gave us reliable results: we could pinpoint all students at the end of November who would later interrupt their studies. Some students continued their studies past Christmas, but dropped out eventually. The algorithm also detected a student who had taken an unauthorized one-week holiday in Greece.
We have built a login follow-up system during autumn 2016. It sends weekly reports of students with a low login rate to the student counsellor. The system is being introduced as this article is being written. We will make more information available later.