Do my students understand? Automated identification of doubts from informal reflections

Abstract

Traditionally, teaching is usually one directional where the instructor imparts knowledge and there is minimal interaction between learners and instructor. With the focus on learner-centered pedagogy, it can be a challenge to provide timely and relevant guidance to individual learners according to their levels of understanding. One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived an approach to automate the identification of doubts from students’ informal reflections through features analysis, word representation and machine learning. Using reflections as a feedback mechanism and aligning it to the weekly course content can pave the way to a promising approach for learner-centered teaching and personalized learning.