Investigating Accuracy of Self-assessment of English Speaking Proficiency Levels by Engineering Students Based on Correlation and Sentiment Analysis

DOI

10.2991/978-94-6463-417-4_8How to use a DOI?

Keywords

self-assessment (SA); evaluation; correlation; sentiment analysis; underestimation

Abstract

The study investigated the accuracy of self-assessment (SA) and possible reasons for inaccuracies using correlation analysis and sentiment analysis based on machine learning. Results show that there was a moderate correlation between self-assessment and teacher assessment, and a general trend of underestimation in student self-assessment, particularly for high-performing and medium-performing students. Results also show that students’ lack of confidence in English language learning, confusion about their own speaking abilities, and lack of a clear understanding of the rating scale are likely to be the reasons for underestimation. Though the accuracy of self-assessment was not validated in this study, it could be argued that SA can still be used as a formative assessment tool to trigger active learning and reflection on own abilities and improve assessment literacy and academic performance through using relevant rating scales or standards.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Linyi Qi
AU  - Xingang Liu
AU  - Jiangqin Zhu
AU  - Jing Wen
PY  - 2024
DA  - 2024/05/07
TI  - Investigating Accuracy of Self-assessment of English Speaking Proficiency Levels by Engineering Students Based on Correlation and Sentiment Analysis
BT  - Proceedings of the 2024 5th International Conference on Big Data and Informatization Education (ICBDIE 2024)
PB  - Atlantis Press
SP  - 77
EP  - 92
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-417-4_8
DO  - 10.2991/978-94-6463-417-4_8
ID  - Qi2024
ER  -