Personalization of Limit Concept Learning Using Neural Networks and Interactive Visualization in Google Colab.

Abstract

The manuscript examines the integration of neural networks and interactive technologies as catalysts for the epistemological deepening of the concept of limit in differential calculus. Through the Google Colab platform, a pedagogical methodology is articulated that orchestrates adaptive content, configured by machine learning algorithms that modulate didactics based on the cognitive idiosyncrasies of the learner; in addition, the proposal includes the use of heuristic visualizations that facilitate analytical introspection on cardinal notions such as continuity, error minimization, and approximations to the limit. In short, this approach, based on a constructivist framework, stands as a disruptive innovation in mathematics teaching, with the potential to reconfigure traditional teaching paradigms.