Abstract
Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution
Highlights
L earning Analytics (LA) is understood as statistical work and computer science applied to educational environments to enhance learning
This study aims to determining to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268)
This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher
Summary
L earning Analytics (LA) is understood as statistical work and computer science applied to educational environments to enhance learning. Educational processes generate much data that can be used to generate actionable insights to innovate the way students learn. It is not hard to find many educational institutions that have a vast amount of data, but it is not utilized to improve educational processes [2] and [3]. A major limitation is a fact that the data is generated after the courses are completed [4], too late to provide timely feedback to the student and to offer adaptative measures to improve their learning. Researchers like Vieira, Parsons & Byrd [5], & Wong [6] claim that currently there is not yet a knowledge field that combines effectively LA and educational theory. Most of what has been developed in this direction are related to online education [7] and [8] and cannot be applied to face-to-face instruction
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