The information society is part of modern life, and algorithmic thinking and programming are relevant to everybody, regardless of educational background. Today’s world needs professionals with computing competencies. Higher education programming classes usually use text-based programming languages to develop programming competencies, usually without success for all students. Traditional programming languages consider syntax barriers that complicate their adoption and usefulness for students. Block-based programming languages such as SCRATCH and ALICE permit the development of programming competencies without text coding and syntax restrictions to solve that issue. There are different research works on SCRATCH experiences, but not many experiences on how to use ALICE overall in higher education. This article aims to evaluate the effectiveness of the ALICE programming language in enhancing programming learning outcomes for university students and addressing the limited number of empirical studies of ALICE in higher education contexts. The research involved a literature review to provide a theoretical foundation, followed by an experimental phase that involved two groups: the control group, which followed the traditional programming instruction method with a text-based programming language, and the experimental group, which used the block-based ALICE environment. The experimental group achieved better results with an average score of 80, while the control group scored 65 points on a 100-point scale. The results obtained highlight the effectiveness of the ALICE environment in supporting the programming learning process for first-level Software Engineering university students.

Vehicle emissions pose significant environmental challenges, particularly in high-altitude regions, where atmospheric conditions amplify pollutant concentrations. This study evaluates CO₂ and hydrocarbon (HC) emissions from 79 gasoline-powered vehicles in Guaranda, Ecuador (2668 m.a.s.l.), by using STATIS, a multivariate statistical method. The vehicles were classified into six model year intervals and tested under idle and dynamic conditions, measuring idle CO₂ and HC (ICD and IHC) and dynamic CO₂ and HC (DCD and DHC). The results showed that vehicles manufactured before 2000 exhibited the highest emissions, with ICD of 3.18% vol. and IHC of 414 ppm, while vehicles produced after 2020 showed significantly lower values (ICD of 0.27% vol. and IHC of 101.44 ppm). Additionally, Chevrolet was the most represented brand, accounting for 41.78% of the analyzed sample, while 34.18% of the vehicles were from the 2010–2015 interval. The STATIS model revealed structural similarities among the 2000–2005, 2016–2019, and post-2020 models, whereas pre-2000 vehicles differed markedly from the 2010–2015 models. Outliers, including older vehicles with low emissions and newer models with unexpectedly high emissions, highlighted the role of maintenance and operational conditions. These findings demonstrate the effectiveness of STATIS in analyzing complex emission patterns and underscore the need for future studies that incorporate variables such as mileage and environmental factors to refine emission mitigation strategies.

Contribution: A systematic literature review on the empirical evidence regarding the usage of programming languages for learning purposes is presented. The review analyzes different methods and tools at different educational levels and with different objectives. Background: Learning programming has gained relevance in the last decade. This is due to the massive presence of programmable elements ranging from computers to toys. Because of this, the interest of researchers on this topic has increased. Questions, such as what to use, in what educational stages to use it, the effectiveness of the method, and the focal objectives for learning programming are questions that do not have obvious answers. Research Questions: 1) What empirical evidence exists on the use of EPLs? 2) In what context is the research performed? 3) How is effectiveness reported in the literature after applying EPLs? 4) What pedagogical goals are achieved by using EPLs? Methodology: Following a formal protocol, automated searches were performed for primary studies from 2007 to 2018. A total of 62 studies were identified, of which 29 were selected and analyzed since they include some type of empirical evidence. Findings: After performing the evaluation, the results support the need for better approaches with empirical evidence when reporting research on the usage of EPLs. Some research opportunities are identified which concerns the used programming languages, the areas or stages of their application, or the need to have more empirical evidence in general and more studies in non-WEIRD (Western, educated, industrialized, rich, and democratic) contexts.

La inteligencia artificial (IA) se ha incorporado rápidamente en la educación superior, generando interrogantes sobre su impacto en el desarrollo del pensamiento crítico de los estudiantes. Mientras algunos estudios sugieren que la IA puede potenciar habilidades de análisis y reflexión, otros advierten sobre los riesgos de una dependencia acrítica de estas herramientas. Esta revisión sistemática tiene como objetivo analizar la relación entre el uso de la inteligencia artificial y el desarrollo del pensamiento crítico en la educación superior. Mediante un enfoque cualitativo y siguiendo el protocolo PRISMA 2020, se realizó una búsqueda en bases de datos como Scopus, Web of Science, ProQuest y SciELO para el período 2020–2026. Se incluyeron 28 estudios en el análisis final. Los resultados demuestran que la relación entre IA y pensamiento crítico no es lineal, sino que depende del encuadre pedagógico. La evidencia converge en cuatro ejes de habilidades: análisis y evaluación, razonamiento y resolución de problemas, autorregulación y juicio ético. Se concluye que la IA fortalece el pensamiento crítico cuando existe mediación formativa, criterios de calidad y alfabetización algorítmica.

The need for knowledgeable programmers has increased, highlighting the importance of strong programming foundations in engineering education. Limited access to high‐quality learning materials and educational opportunities presents challenges unrelated to information and communication technology (ICT) field (non‐ICT‐related) students in acquiring programming skills. Educational programming languages (EPLs) such as App Lab have gained popularity as they offer an accessible platform for students to learn programming fundamentals in a visual and interactive manner. This paper examines the impact of the EPL called App Lab on the development of fundamental programming skills among non‐ICT‐related field engineering college students. We conducted a quasiexperimental research study using a single‐blinded, nonequivalent control group pretest–posttest design. The study included 56 participants, all of whom were enrolled in the Environmental Engineering and Biotechnology program at the State University of Milagro (UNEMI), Ecuador. The experimental group consisted of 26 students, while the control group comprised 30 students. The assessment process involved the administration of a battery of 200 questions before and after the intervention. The intervention involved the use of App Lab as an EPL and lasted for a duration of 3 weeks exclusively for the experimental group, while the control group followed their usual tutoring program. The study results showed that students who received EPL‐mediated learning with App Lab had significant increase in their programming skills. App Lab demonstrated a positive impact, particularly among male students who reported Internet usage, as well as in advanced programming topics including loops, lists, and functions, when compared to their female counterparts.

This article examines patterns of enrollment, academic performance, and dropout among women in engineering programs at the State University of Milagro (UNEMI), Ecuador, between 2016 and 2023. Using a quantitative approach that combines descriptive statistics with exploratory factor analysis, the study identifies critical obstacles affecting the academic persistence of female students. Results show a 291% increase in female enrollment following the implementation of institutional policies focused on inclusion and the expansion of virtual education, reflecting the cumulative growth rate in female enrollment from 296 students in 2016 to 1158 in 2023. However, this growth coincided with a decline in pass rates and an increase in dropout levels. Among the main factors influencing student attrition are financial limitations associated with transportation, access to technology, and study materials. Additional elements include motivational aspects and insufficient academic preparation. The findings highlight the importance of strengthening institutional strategies that promote academic equity, create inclusive learning environments, and respond effectively to the personal and structural challenges that women face in engineering education. This study contributes empirical evidence that can inform university policies and public initiatives aimed at fostering greater participation and success for women in science and technology fields.