Mg. Gutiérrez-Valenzuela, Mariella

This article presents the LGBTQIA phobia dataset (augmented and balanced), a curated Spanish-language corpus designed to support research on the automated detection of discriminatory and hateful language targeting LGBTQIA+ communities in digital environments. The dataset comprises 1,000 short textual phrases collected from publicly available content on the web, X/Twitter, Instagram, TikTok, and YouTube comments. Each instance was manually annotated by three independent raters using a binary labeling scheme (1 is LGBTQIA phobic; 0 is non-LGBTQIA phobic). To improve robustness and reproducibility, text augmentation and class balancing via under sampling were applied. The final dataset is evenly distributed across classes and released under a Creative Commons Attribution 4.0 International license through Zenodo. This resource aims to bridge the gap in Spanish-language datasets for hate speech detection, aiding future research in content moderation, bias analysis, and the creation of inclusive artificial intelligence systems.

Objective: The objective of this paper is to present preliminary work on the development of an EduChatBot tool and the measurement of the effects of its use aimed at providing effective feedback to programming course students. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT3.5 and is tasked with assisting and providing timely positive feedback to students in computer science programming courses at UCSC. Methods/Analysis: The proposed method consists of four stages: (1) Immersion in the feedback and Large Language Models (LLMs) topic; (2) Development of tutorBot+ prototypes in both non-conversational and conversational versions; (3) Experiment design; and (4) Intervention and evaluation. The first stage involves a literature review on feedback and learning, the use of intelligent tutors in the educational context, as well as the topics of LLMs and chatGPT. The second and third stages detail the development of tutorBot+ in its two versions, and the final stage lays the foundation for a quasi-experimental study involving students in the curriculum activities of Programming Workshop and Database Workshop, focusing on learning outcomes related to the development of computational thinking skills, and facilitating the use and measurement of the tool’s effects. Findings: The preliminary results of this work are promising, as two functional prototypes of tutorBot+ have been developed for both the non-conversational and conversational versions. Additionally, there is ongoing exploration into the possibility of creating a domain-specific model based on pretrained models for programming, integrating tutorBot+ with other platforms, and designing an experiment to measure student performance, motivation, and the tool’s effectiveness.

The main objective of this article is to present and validate a statistical model (N = 3,152) to predict the dropout of students from the School of Engineering of the Universidad Católica de la Santísima Concepción (UCSC) in Chile. Student droupout in engineering is a generalized and multifactorial phenomenon, even more so when the student can use his or her university access score for a period of two years. In the UCSC, a distinction is made between formal and nonformal droupout. The information collection methodology in this study included the survey administered by the Department of Evaluation, Measurement and Educational Registry of Chile (DEMRE) and input from the Directorate of Admission and Academic Registration of the UCSC. Within the analysis groups were students who formally resigned and were analyzed according to the reasons they gave for leaving; the other group was constituted by students who did not formalize their abandonment, deserters. Subsequently, a logistic regression analysis was applied to determine which variables would best explain the phenomenon of droupout. Among the main factors are gender (GENDER), program (AU), cumulative average score (PPA_SCORE), mathematics score of the university selection test (PSU_MATH_SCORE), mother education level (EDU_MOM), progression rate of student in engineering program (PROGRESSION_RATE) and socioeconomic quintile of student (QUINTILE). The performance of the prediction model shows an accuracy (88.53%) and precision (88.69%), which is a very encouraging result in relation to the performance of the studies reviewed in the literature.

The COVID-19 pandemic affected people’s mobility and access to urban activities. When the contagion was at a community level, quarantine measures were taken, causing population immobility. The lack of alternatives significantly altered the satisfaction of people’s basic needs. The objective of this article was to explore and generate real accessibility indicators for goods and services, in addition to the levels of spatial satisfaction of the population, at a regional level in the metropolitan area of Concepción, Chile. To focus on citizens’ social welfare, social geomarketing was applied as the method, obtaining the delimitation of accessibility areas for goods and services through population surveys and the delimited spatial decelerated satisfaction. Pre-pandemic and during-pandemic situations were evaluated. The results showed an improvement in the delimitation of accessibility areas of goods and services, as the citizens’ preferences as consumers were included, revealing an increment during the pandemic, especially in the food typology. In the same way, the existence of geospatial satisfaction and its increment under the pandemic context when accessing the diverse facilities that offer these kinds of goods was confirmed. In conclusion, the satisfaction areas were useful for analyzing urban form designs and focusing them to promote revitalization, as well as for inclusive and sustainable urbanization and proactive measures to make urban areas more resilient to natural or human risks, incorporating the role of geospatial tools for promoting sustainable urban development.

This paper uses a knowledge-based approach to querying heterogeneous spatial databases based on an ontology and conceptual and attribute similarities. The ontology, which may be independent of the databases, expands and filters a user query. Then, queries are translated into a formal specification of entity classes, which are compared against definitions in databases. This process is carried out by determining the conceptual similarity between entities in a user ontology and by comparing these entities in the ontology with entities in the conceptual models of databases. In addition, the specification of a query is done not only by identifying entity classes but also by considering constraints based on attribute values. The paper describes the system architecture and presents a case study with data from a forestry information system.