Examinando por Autor "Montalvo, Soto"

Mostrando 1 - 3 de 3

Building an Educational Platform Using NLP: A Case Study in Teaching Finance
(Verlag der Technischen Universität Graz, 2018-10-28) Montalvo, Soto; Palomo, Jesus; de la Orden, Carmen
Information overload is one of the main challenges in the current educational context, where the Internet has become a major source of information. According to the European Space for Higher Education, students must now be more autonomous and creative, with lecturers being required to provide guidance and supervision. Guiding students to search and read news related to subjects that are being studied in class has proven to be an effective technique in improving motivation, because students appreciate the relevance of the topics being studied in real world examples. However, one of the main drawbacks of this teaching practice is the amount of time that lecturers and students need for searching relevant and useful information on different subjects. The objective of our research is to demonstrate the usefulness of a complementary teaching tool in the traditional educational classroom. It is a new educational platform that combines Artificial Intelligence techniques with the expertise provided by lecturers. It automatically compiles information from different sources and presents only relevant breaking news classified into different subjects and topics. It has been tested on a Finance course, where being continually informed about the latest economic and financial news is an important part of the teaching process, specially for certain key financial concepts. The utility of the platform has been studied by conducting student surveys. The results confirm that using the platform had a positive impact on improving students' motivation and boost the learning processes. This research provides evidence about effectiveness of the new educational complement to traditional teaching methods in classrooms. Also, it demonstrates the improvement on the knowledge transfer within an environment of information overload.
Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models
(2023-12-02) Villaplana, Aitana; Martínez, Raquel; Montalvo, Soto
Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results.
NATURAL LANGUAGE PROCESSING APPLIED FOR TEACHING EVALUATION
(ICERI2022 Proceedings, 2022-11-09) Montalvo, Soto; Rodríguez, Miguel Ángel; Cabido, Raúl; Concha, David
In the context of higher education, the improvement of teaching quality is a constant challenge. Student ratings of teaching are often considered fundamental for measuring the quality of teaching, educational development, and the enhancement of student learning. In the university context, the institution develops methods for measuring teaching quality and teacher effectiveness defining the process of collecting data and making judgments. However, for decades, there has been a debate about whether student ratings of teaching be trusted. Different studies have shown that several factors influence student ratings as teacher body language, timing, or student fatigue, among others. On the other hand, student ratings and student learning are unrelated. The surveys must become the starting point for critical discourse on effective teaching practices. Teaching thousands of students without engaging them in discussions about their learning experiences is not rational. In this sense, we propose an easy way to collect the students' opinions for teaching evaluation. We developed an anonymous online form, with several questions about different general aspects, shared by all subjects, such as theory, practice, evaluation, etc., and a final question to get the general evaluation of the subject. Each question must be answered with a numerical value between 1 and 5; the student can introduce text to explain or justify the numerical value. The proposed form is sufficiently general to be applied to any subject. Still, at the same time, with the part of text answers that accompanies each question, plus the general open question, the form allows the singularities of each subject to be considered. The objective is to know the student's opinion about some aspects of subjects through a general question. Concretely, the system aims at understanding all that a student wants to outline about the subject, good or bad, and the possible correlation or not between numerical and text responses. The proposed system automatically identifies the sentiment of text answers through Natural Language Processing techniques. We collected student feedback for four different subjects from 2-degree programs. First, we could see that approximately 40% of the enrolled students responded to the form. In the case of the university's institutional surveys, they are usually done by more students because they are mandatory, but this means that the answers they give could be not sincere. On the other hand, we have taken as a gold standard the numerical response of the students to the open-ended question about the general assessment, and we have contrasted it with the results of the automatic sentiment analysis. A significant correlation was found for negative evaluations, whereas the less was for positive ones. The students indicated that they liked the subject according to the numerical evaluation but did not express the same in the text response. Consequently, it can indicate two things: either that the sentiment analyzer needs to be adjusted or that the students are inconsistent in their answers. Further work will be done on the sentiment analysis system, reviewing the vocabulary used by the students, extending the study to all the questions on the form, and analyzing whether the students' opinions on the overall rating are different from those stated on the form itself.