According to the researchers, this continuous monitoring helps reduce the time between the first signs of risk and the system's intervention to help preventThe technology has been pilot tested with 581 students enrolled on first-semester courses in a number of the UOC's Faculty of Economics and Business's bachelor's degree programs and has been found to reduce dropout rates and increase participation during the semester.
If a high level of risk is detected, the system activates the applicable intervention mechanisms in the form of messages to students."This prediction, although very useful for students, has shortcomings, mainly because monitoring is limited to certain checkpoints after each activity , which means that the associated intervention can be too late, when the student has already dropped out for the year," said Bañeres.
In other words, in order to confirm that a student is really at risk of dropping out and activate the appropriate intervention mechanisms, the student must remain in the category of being at risk of dropping out for a given consecutive number of days, which is specified for each activity. If a student is at high risk of dropout, they are sent an automatic intervention message.
In addition, it is a scalable tool that allows teaching staff to comprehensively monitor courses with a high number of students."For example, one of the pilot studies was carried out in a course with 1,500 students, and the system allowed teaching staff to monitor students at risk of dropping out without overloading the coordinating professor or the course instructors," he said.