An Analysis of the use of Good Feedback Practices in Online Learning Courses

Abstract

Feedback is an essential component of any learning experience. It allows students to identify gaps in their learning and improve their self-regulation. However, providing useful feedback is a challenging and time-consuming task. In digital learning environments, this challenge is even more significant due to a large number of students. Thus, this paper reports on the findings of an analysis of the quality of feedback provided by instructors in an online course. The paper also proposes a supervised machine learning algorithm that can identify the presence of good practices in feedback messages sent to students in a digital learning environment. The results reveal the most commonly used kinds of feedback and how to identify them automatically. The results of the study could potentially be used to improve the quality of the feedback provided by instructors in online education.