Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2021

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.

Keywords

Doubt identification, sentic computing, learner-centered pedagogy, text analytics

Discipline

Databases and Information Systems | Educational Assessment, Evaluation, and Research | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Research and Practice in Technology Enhanced Learning

Volume

16

Issue

1

First Page

1

Last Page

24

ISSN

1793-2068

Identifier

10.1186/s41039-021-00149-9

Publisher

SpringerOpen

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1186/s41039-021-00149-9

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