Student Variability in Learning Advanced Physics

TL;DR

This paper introduces an iterative online learning system tailored to individual student paces in advanced physics, demonstrating its effectiveness in helping slower students catch up through personalized content delivery.

Contribution

The study presents the development and implementation of iOLM, a personalized online learning platform that adapts to students' learning speeds in a university physics course.

Findings

Wide variation in student learning paces observed

Personalized content helps weaker students catch up

No correlation between pace and performance after intervention

Abstract

Learning of advanced physics, requires a combination of empirical, conceptual and theoretical understanding. Students use a combination of these approaches to learn new material. Each student has different prior knowledge and will master new material at a different pace. However, conventional classroom teaching usually does not accommodate the different learning paces of students. To both, study and address this issue, we developed an iterative Online Learning Machine (iOLM), which provides new learning content to each student based on their individual learning pace and tracks their progress individually. The iOLM learning module was implemented using server side web software (php) to supplement the undergraduate course in electromagnetic waves for majors in physics in their second year. This approach follows the hybrid online learning model. Students had to complete a section of the course using iOLM, which was only presented online. The data obtained for this class showed a wide spread of learning paces, ranging from 0.1 to 0.5, where 1 is the maximum pace allowed by iOLM and 0 the lowest. The mean was mu=0.25, with a standard deviation of sigma=0.12. While the pretest showed a positive correlation between the student's pace and performance, the postest had zero correlation, indicating that giving more time and content to weaker students allows them to catch up.