Open AccessA Data-Driven Emotion Model for English Learners Based on Machine Learning
Author(s) -
Zhizhen Zheng,
Si Na Kew
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i08.22127
Subject(s) - confusion , artificial intelligence , correctness , computer science , test (biology) , machine learning , natural language processing , psychology , paleontology , psychoanalysis , biology , programming language
Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning.