Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions
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 po...
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| Format: | Article |
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Dr. Mahmoud Mansour publication
2023-11-01
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| Series: | مجله علوم روانشناختی |
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| Online Access: | http://psychologicalscience.ir/article-1-2350-en.pdf |
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| author | Nora Darjazini Mohammad Hossein Zarghami Reza Ghorban Jahromi Leila Shobeiry |
| author_facet | Nora Darjazini Mohammad Hossein Zarghami Reza Ghorban Jahromi Leila Shobeiry |
| author_sort | Nora Darjazini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7a55836ce84d4f7bab1341fa522876e7 |
| institution | DOAJ |
| issn | 1735-7462 2676-6639 |
| language | fas |
| publishDate | 2023-11-01 |
| publisher | Dr. Mahmoud Mansour publication |
| record_format | Article |
| series | مجله علوم روانشناختی |
| spelling | doaj-art-7a55836ce84d4f7bab1341fa522876e72025-08-20T02:40:06ZfasDr. Mahmoud Mansour publicationمجله علوم روانشناختی1735-74622676-66392023-11-0123141179197Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directionsNora Darjazini0Mohammad Hossein Zarghami1Reza Ghorban Jahromi2Leila Shobeiry3 Ph.D Candidate in Educational Psychology, Science and Research Unit, Islamic Azad University, Tehran, Iran Assistant Professor, Behavioral Sciences Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran Assistant Professor, Department of Educational and Personality Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran Assistant Professor, Department of French Language, Faculty of Literature, Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran. 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.http://psychologicalscience.ir/article-1-2350-en.pdfmachine learningreading comprehensionnatural language processingsyntactic and lexical featureslearning english |
| spellingShingle | Nora Darjazini Mohammad Hossein Zarghami Reza Ghorban Jahromi Leila Shobeiry Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions مجله علوم روانشناختی machine learning reading comprehension natural language processing syntactic and lexical features learning english |
| title | Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions |
| title_full | Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions |
| title_fullStr | Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions |
| title_full_unstemmed | Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions |
| title_short | Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions |
| title_sort | artificial intelligence machine learning in the psychology of learning unveiling new insights and directions |
| topic | machine learning reading comprehension natural language processing syntactic and lexical features learning english |
| url | http://psychologicalscience.ir/article-1-2350-en.pdf |
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