Experiences in the use of an adaptive intelligent system to enhance online learners' performance: a case study in Economics and Business courses

Abstract

Abstract Several tools and resources have been developed in the past years to enhance the teaching and learning process. Most of them are focused on the process itself, but few focus on the assessment process to detect at-risk learners for later acting through feedback to support them to succeed and pass the course. This research paper presents a case study using an adaptive system called Learning Intelligent System (LIS). The system includes an Early Warning System and tested in a fully online university to increase learners’ performance, reduce dropout, and ensure proper feedback to guide learners. LIS also aims to help teachers to detect critical cases to act on time with learners. The system has been tested in two first-year courses in the fully online BSc of Economics and Business at the Universitat Oberta de Catalunya. A total of 552 learners were participating in the case study. On the one hand, results show that performance is better than in previous semesters when using it. On the other hand, results show that learners' perception of effectiveness is higher, and learners are willing to continue using the system in the following semesters because it becomes beneficial for them.

Keywords

• Online learning
• Early warning system
• Feedback
• Artificial intelligence
• Performance
• Dropout