Integración de Tecnologías Digitales y Analítica del Aprendizaje para la Enseñanza Personalizada del Cálculo Diferencial en Educación Superior

John Eduardo Guiñanzaca Vaca; Pedro Daniel Armijo Guamán; Eduardo Agustín Serrano Estrada; Oscar Gabriel Toapanta Cunalata; Carlos Fernando Heredia Villamarín

doi:10.70577/ASCE/478.502/2025

Authors

John Eduardo Guiñanzaca Vaca Universidad Nacional de Educación https://orcid.org/0000-0003-1801-7380
Pedro Daniel Armijo Guamán Independiente https://orcid.org/0009-0008-9531-7647
Eduardo Agustín Serrano Estrada Humane Tecnológico Universitario de Negocios https://orcid.org/0009-0002-4300-0601
Oscar Gabriel Toapanta Cunalata Instituto Superior Tecnológico Pelileo https://orcid.org/0000-0002-5816-1785
Carlos Fernando Heredia Villamarín Ministerio de Educación del Ecuador https://orcid.org/0009-0006-4369-1915

DOI:

https://doi.org/10.70577/ASCE/478.502/2025

Keywords:

Educational technology, learning analytics, learning personalization, differential calculus, higher education, automated feedback, adaptive learning paths.

Abstract

The research aims to study how digital technology and learning analytics enable personalized instruction in differential calculus for higher education students. It seeks to assess the impact of automated feedback combined with adaptive learning pathways on student achievement and motivation by tracking interaction, performance, and academic progress. A mixed-method research approach was used to achieve the goal. It included quantitative data collection and analysis from educational platforms, as well as qualitative semi-structured interviews and questionnaires with students and faculty. 180 first-year students were divided into an experimental group with technology-enhanced instruction and a control group with traditional teaching. The students were enrolled in a Differential Calculus course at two public universities. The study lasted 16 weeks, one academic semester.The experimental group achieved significantly higher performance in Differential Calculus assessments compared to the control group (average score of 85% vs. 74%; p < 0.01). The system effectively adjusted the level of challenge after three consecutive errors, reducing performance stagnation by 20% in digital learning pathways.Students attributed increased motivation, autonomy, and confidence to immediate feedback and personalized learning routes in interviews. Positive outcomes noted by faculty members included reduced grading time and the ability to focus tutorial sessions on challenging concepts. The integration of digital technology and learning analytics enables personalized guidance in Differential Calculus, fostering a student-centered approach. This is significant because Differential Calculus is a notoriously abstract subject. Synergy can enhance learning and reduce dropout rates, developing graduates with strong analytical skills. The model should be adapted to other branches of mathematics and different educational contexts.

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