Use of an adaptive intelligent system to enhance online learners performance

The use of technological systems to improve the online teaching and learning process has evolved very fast in recent years. Tools and resources to support how teachers teach and how learners learn by using Information and Communication Technology (ICT) have been deeply analyzed in the literature. In this educational process, teachers design a broad diversity of learning activities to promote communication skills when interacting with learners through the virtual learning environment.

The research paper that we present today talks about this topic, which is written by Amal Elasri-Ejjaberi, member of Management & e-Learning research group (MeL), together with Ana-Elena Guerrero-Roldán, M. Elena Rodríguez-González, David Bañeres and Pau Cortadas. The article was published in International Journal of Educational Technology in Higher Education in 2021.

Objectives and Methodology

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.

Results and Conclusions

The results indicated a high level of learner satisfaction with the effectiveness and usefulness of the LIS system, with a majority expressing willingness to use it in future courses. This positive feedback suggests that the LIS system plays a valuable role in supporting learners in managing self-efficacy and self-regulation, particularly among younger age groups.

Furthermore, the analysis of learners’ performance data revealed that the use of the LIS system led to improved outcomes in the courses studied. Learners who participated in testing the LIS system demonstrated better performance compared to those who did not, as well as compared to the previous semester’s performance.

Additionally, the regression models developed for the courses provided valuable insights into the factors influencing learners’ final marks. The models highlighted the importance of variables such as ALA grades, enrollment status, and course activity completion in predicting learner performance.

Overall, the study’s conclusions emphasize the positive impact of the LIS system on learner engagement, performance, and satisfaction in online Economics and Business courses. The findings support the continued integration of adaptive intelligent systems like LIS to enhance the online learning experience and improve educational outcomes for students.

Consult the study

You can consult the full article by clicking here.