Adaptative Learning in a Calculus Course for Students of Engineer

The recent increase in applications exploiting artificial intelligence has provided a wide range of new possibilities in most aspects of human life. Within the educational field, one of the goals that seems plausible nowadays is providing students with a personalized learning experience by creating algorithms that tailor themselves to meet individual needs. This approach is called adaptive learning, and it provides several advantages for learners, such as learning-time optimization, knowledge and skills reinforcement, and delivery of up-to-date content. On their end, the teacher gets detailed analytics about the progress, struggles, and achievements of students, which allows for specific reinforcement and reshaping of lesson plans. In this paper, the learning experience from an adaptive learning implementation of a first-year Calculus course for engineering students is analyzed based on quantitative and qualitative results. The former is performed by contrasting the outcome of traditional written evaluations and overall grades for the course, while the latter uses a sentiment analysis obtained from student's comments. The comparison takes place between groups that used the adaptive platform Realizeit® and control groups with a traditional teaching methodology. © 2025 IEEE.

Adaptative Learning in a Calculus Course for Students of Engineer Academic Article in Scopus