Enter your email address
Submit
Write your message
Volume 23, Issue 141 (11-2024)
Journal of Psychological Science 2024, 23(141): 179-197 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Darjazini N, Zarghami M H, Ghorban Jahromi R, Shobeiry L. (2024). Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions. Journal of Psychological Science. 23(141), 179-197. doi:10.52547/JPS.23.141.2223
URL: http://psychologicalscience.ir/article-1-2350-en.html
URL: http://psychologicalscience.ir/article-1-2350-en.html
Assistant Professor, Behavioral Sciences Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran , zar100@gmail.com
Abstract: (1073 Views)
Background: The intersection of artificial intelligence (AI), psychology and applied linguistics particularly in the realm of language learning, has opened up a fascinating avenue for exploring the intricate processes and mechanisms underlying human cognition. Machine learning algorithms have the potential to shed light on the fundamental principles of learning language specially on reading comprehension as the core language learning variable.
Aims: In this research, we used supervised machine learning techniques in order to discover the most important syntactic and lexical features affecting the reading comprehension of English language learners.
Methods: The design of present study is causal comparative type (ex post facto). the population includes all second secondary level students who learn English in language training institutions. To select the participants, language training institutes in Tehran were referred. The participants (n=360) answered BALA exam (Young, 2022) questions in written and spoken form.
Results: 260 features were extracted from the computer texts prepared from the speech and writing learners responses by natural language processing (NLP) algorithms. We used learning models of decision tree, nearest neighbor, support vector method, neural network and regularized linear method to predict reading comprehension using extracted linguistic features.
Conclusion: The results showed that the variance of language learners' reading comprehension can be well modeled using the extracted grammatical and lexical features, and in addition, twenty features that play the most important role in explaining the variance were identified. This study shows that ML methods can determine the detailed investigation of language processes related to reading comprehension.
Aims: In this research, we used supervised machine learning techniques in order to discover the most important syntactic and lexical features affecting the reading comprehension of English language learners.
Methods: The design of present study is causal comparative type (ex post facto). the population includes all second secondary level students who learn English in language training institutions. To select the participants, language training institutes in Tehran were referred. The participants (n=360) answered BALA exam (Young, 2022) questions in written and spoken form.
Results: 260 features were extracted from the computer texts prepared from the speech and writing learners responses by natural language processing (NLP) algorithms. We used learning models of decision tree, nearest neighbor, support vector method, neural network and regularized linear method to predict reading comprehension using extracted linguistic features.
Conclusion: The results showed that the variance of language learners' reading comprehension can be well modeled using the extracted grammatical and lexical features, and in addition, twenty features that play the most important role in explaining the variance were identified. This study shows that ML methods can determine the detailed investigation of language processes related to reading comprehension.
Keywords: machine learning, reading comprehension, natural language processing, syntactic and lexical features, learning english
Type of Study: Research |
Subject:
Special
Received: 2024/02/18 | Accepted: 2024/04/19 | Published: 2024/11/21
Received: 2024/02/18 | Accepted: 2024/04/19 | Published: 2024/11/21
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. |
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)