Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach
Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Abstract
Abstract Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible 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 a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ informal reflections. The encouraging results clearly show that the hybrid approach has the potential to be adopted in the real-world doubt detection. Using reflections as a feedback mechanism and automated doubt detection can pave the way to a promising approach for learner-centered teaching and personalized learning.