Students' academic performance is a key factor for educational institutions and society, which is an important indicator of the quality of the teaching-learning process and the appropriation of knowledge. Its analysis allows an understanding of the behavior of students and teachers, generating valuable knowledge for making timely academic decisions. In this study, the following phases were carried out: (i) identification of factors that influence the academic performance of engineering university students, (ii) early prediction of academic success, and (iii) identification of use patterns in a virtual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data mining techniques, using academic and socioeconomic data and interactions (resources and activities) with the VLE. The tools and programming languages ​​used were Pentaho Data Integration for data integration and processing; Jupyter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE resources such as files, links, and activities such as participation in forums are factors that lead to good academic performance. On the other hand, it was possible to make predictions of academic success (pass or not) with an accuracy greater than 95% and to identify the main patterns of use of the VLE. The group with excellent academic performance (grades 9 to 10) is recognized for using file-type resources and high participation in class and forum activities.

Over the last decade, Zoltán Dörnyei’s L2 Motivational Self-System (L2MSS) theoretical framework has been used to explore the relationship between successful second language learning and learner motivation in a myriad of language learning contexts. However, not many studies have been carried out in developing countries. Through the lens of L2MSS’s three main components: Ideal L2 self, Ought to L2 self, and Learning experience, this study explores Language Learning Motivated Behavior (LLMB) in an Ecuadorian EFL setting. The article is part of a university research project. One hundred twenty-eight senior high school students completed a questionnaire drawn from previous research studies on the L2MSS. The results confirm that the Ideal L2 Self is a strong predictor of language learning motivation in Ecuador, validating this component as one of the highest-order motivational forces across different populations of L2 learners. Concerning its counterpart, the Ought to L2 self, it was not found a significant correlation between this variable and the criterion measure (LLMB), determining that the external force of the L2MSS presents socio-cultural variances. The Learning experience component also has a noticeable impact on motivated behavior in the context of the study.

El objetivo principal de la investigación fue establecer los beneficios del ciclismo de montaña turístico para el desarrollo local sostenible en la ciudad de Ibarra de Ecuador. El tipo de investigación fue descriptiva y los instrumentos empleados fueron registro de campo mediante encuestas. El cuestionario dirigido a una muestra de 126 participantes de los 420 asistentes en la Vuelta al Imbabura del año 2015. La validación del método se realizó mediante el juicio de expertos seleccionados con el coeficiente de Conocimiento (Kc) y la confiabilidad interna mediante el coeficiente de Alfa de Cronbach del programa SPSS. Como resultado, se evidenció beneficios económicos, sociales, ambientales que influyeron en el desarrollo de la ciudad como uno de los destinos turísticos de Ecuador. Se concluyó que el ciclismo de montaña promovió los principios basados en la sustentabilidad favoreciendo el desarrollo local mediante actividades relacionadas con el turismo que genera este deporte.

Monuments and public sculptures embody collective memory, values, and identity. This study analyses the representation of women in public sculptures in Ibarra, Ecuador, and evaluates citizens’ recognition of the historical figures depicted. A mixed-method, cross-sectional design was employed. An urban inventory was conducted (N = 124 sculptures), and questionnaires were administered in situ to 1200 adult residents using non-probability intercept sampling (100 surveys at each of the 12 female monuments). The results reveal a marked disparity: 55.6% of the sculptures represent men, compared with only 9.7% representing women. Recognition is minimal: 98.6% of respondents did not identify the person represented, and 95.1% reported no knowledge of her history. These findings suggest that the underrepresentation of women in public art reflects enduring structural and cultural gender inequalities. The limited presence of female monuments contributes to the erasure of women’s legacy from collective memory and perpetuates the perception of public space as historically male-dominated. Framed within the literature on gender and monuments as devices of social memory, the study advocates for inclusive commemorative policies and interpretive strategies. Limitations include the non-random sampling and single-city scope; future research should expand comparisons across cities and assess potential interventions.