Same-age Tutoring in Maths using ICT: From Computational Math Students to Chemistry Students

En este trabajo exponemos un proyecto educativo basado en la tutorización por pares entre estudiantado de la misma edad (same-age tutoring) que cursan la asignatura de Matemáticas I pero en dos grados diferentes como son el grado en Matemática Compu- tacional y el grado en Química. Los contenidos de la asignatura son los mismos en los dos grados pero tanto el estudiantado como los propios grados tienen características completamente distintas. Por tanto, el estudiantado del grado en Matemática Computacional ejercerá de estudiantado tutor y el del grado en Química de estudiantado tutorizado. En las sesiones de tutorización utilizamos el aprendizaje semipresencial (blended learning) y se introducen las herramientas TIC en un doble sentido; con el fin de gestionar la comunicación, distribuir materiales docentes y realizar trabajo cooperativo, y con el objetivo de introducir un programa de cálculo simbólico que ayude a la comprensión de conceptos matem ́aticos abstractos.

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remains a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction, and cross-disciplinary scientific communication.

A Data Science Enhanced Framework for Applied and Computational Math

[This Proceedings paper was revised and published in the 2018 issue of the journal Issues in Informing Science and Information Technology, Volume 15] The primary objective of this research was to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. The framework can help students grasp new mathematical applications by comparing them to a common reference model. In this research, we measured the most frequent words used in a sample of Math and Computer Science books. We integrated these words with those obtained in an earlier study, from which we had constructed the original Computational Math scale. The enhanced framework improves our Computational Math scale by integrating selected concepts from the field of Data Science. The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations.

A Data Science Enhanced Framework for Applied and Computational Math

Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science. Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations. Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.