LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification
The incorporation of artificial intelligence in educational contexts has significantly transformed the support provided to students facing learning difficulties, facilitating both the management of their educational process and their emotions. Additionally, online comments play a vital role in under...
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MDPI AG
2025-05-01
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| Series: | Multimodal Technologies and Interaction |
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| Online Access: | https://www.mdpi.com/2414-4088/9/5/44 |
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| author | Anabel Pilicita Enrique Barra |
| author_facet | Anabel Pilicita Enrique Barra |
| author_sort | Anabel Pilicita |
| collection | DOAJ |
| description | The incorporation of artificial intelligence in educational contexts has significantly transformed the support provided to students facing learning difficulties, facilitating both the management of their educational process and their emotions. Additionally, online comments play a vital role in understanding student feelings. Analyzing comments on social media platforms can help identify students in vulnerable situations so that timely interventions can be implemented. However, manually analyzing student-generated content on social media platforms is challenging due to the large amount of data and the frequency with which it is posted. In this sense, the recent revolution in artificial intelligence, marked by the implementation of powerful large language models (LLMs), may contribute to the classification of student comments. This study compared the effectiveness of a supervised learning approach using five different LLMs: bert-base-uncased, roberta-base, gpt-4o-mini-2024-07-18, gpt-3.5-turbo-0125, and gpt-neo-125m. The evaluation was carried out after fine-tuning them specifically to classify student comments on social media platforms with anxiety/depression or neutral labels. The results obtained were as follows: gpt-4o-mini-2024-07-18 and gpt-3.5-turbo-0125 obtained 98.93%, roberta-base 98.14%, bert-base-uncased 97.13%, and gpt-neo-125m 96.43%. Therefore, when comparing the effectiveness of these models, it was determined that all LLMs performed well in this classification task. |
| format | Article |
| id | doaj-art-9fae3031873745c0b504bb9cdcaf3a11 |
| institution | Kabale University |
| issn | 2414-4088 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Multimodal Technologies and Interaction |
| spelling | doaj-art-9fae3031873745c0b504bb9cdcaf3a112025-08-20T03:48:02ZengMDPI AGMultimodal Technologies and Interaction2414-40882025-05-01954410.3390/mti9050044LLMs in Education: Evaluation GPT and BERT Models in Student Comment ClassificationAnabel Pilicita0Enrique Barra1Departamento de Ingeniería de Sistemas Telemáticos, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartamento de Ingeniería de Sistemas Telemáticos, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, SpainThe incorporation of artificial intelligence in educational contexts has significantly transformed the support provided to students facing learning difficulties, facilitating both the management of their educational process and their emotions. Additionally, online comments play a vital role in understanding student feelings. Analyzing comments on social media platforms can help identify students in vulnerable situations so that timely interventions can be implemented. However, manually analyzing student-generated content on social media platforms is challenging due to the large amount of data and the frequency with which it is posted. In this sense, the recent revolution in artificial intelligence, marked by the implementation of powerful large language models (LLMs), may contribute to the classification of student comments. This study compared the effectiveness of a supervised learning approach using five different LLMs: bert-base-uncased, roberta-base, gpt-4o-mini-2024-07-18, gpt-3.5-turbo-0125, and gpt-neo-125m. The evaluation was carried out after fine-tuning them specifically to classify student comments on social media platforms with anxiety/depression or neutral labels. The results obtained were as follows: gpt-4o-mini-2024-07-18 and gpt-3.5-turbo-0125 obtained 98.93%, roberta-base 98.14%, bert-base-uncased 97.13%, and gpt-neo-125m 96.43%. Therefore, when comparing the effectiveness of these models, it was determined that all LLMs performed well in this classification task.https://www.mdpi.com/2414-4088/9/5/44LLMsNLPtransformerseducationBERTGPT |
| spellingShingle | Anabel Pilicita Enrique Barra LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification Multimodal Technologies and Interaction LLMs NLP transformers education BERT GPT |
| title | LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification |
| title_full | LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification |
| title_fullStr | LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification |
| title_full_unstemmed | LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification |
| title_short | LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification |
| title_sort | llms in education evaluation gpt and bert models in student comment classification |
| topic | LLMs NLP transformers education BERT GPT |
| url | https://www.mdpi.com/2414-4088/9/5/44 |
| work_keys_str_mv | AT anabelpilicita llmsineducationevaluationgptandbertmodelsinstudentcommentclassification AT enriquebarra llmsineducationevaluationgptandbertmodelsinstudentcommentclassification |