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Analysis of institutional authors

Pilicita, AnabelAuthorBarra, EnriqueCorresponding Author

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June 3, 2025
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LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification

Publicated to: Multimodal Technologies and Interaction. 9 (5): 44- - 2025-05-12 9(5), DOI: 10.3390/mti9050044

Authors:

Pilicita, A; Barra, E
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Affiliations

Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac, Dept Ingn Sistemas Telemat, Madrid 28040, Spain - Author

Abstract

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.
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Keywords

AnxietBertEducationGpGptLlmsMedical-studentsNlpTransformers

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Multimodal Technologies and Interaction due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 16/32, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Cybernetics. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Computer Networks and Communications.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-09:

  • Google Scholar: 3
  • WoS: 1
  • Scopus: 1
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 32 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/95151/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 10
    • Downloads: 7
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    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PILICITA GARRIDO, ANABEL) and Last Author (BARRA ARIAS, ENRIQUE).

    the author responsible for correspondence tasks has been BARRA ARIAS, ENRIQUE.

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    Awards linked to the item

    The authors would like to acknowledge the support of the FUN4DATE (PID2022-136684OB-C22) project funded by the Spanish Agencia Estatal de Investigacion (AEI) 10.13039/501100011033 and TUCAN6-CM (TEC-2024/COM-460), funded by CM (ORDEN 5696/2024).
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