Developing programming competencies is essential for systems, information science, computer science, and electrical engineering students. Engineering students usually face the complexity of working with programming languages that demand compliance with syntactic and semantic rules, which typically represent a daunting task for novice students. Watching textual messages on the screen only, like the classic “hello world” is no longer attractive in the current information society, a missing motivation and possible obstacle to developing programming competencies. Students would like to interact with hardware and appreciate environmental reactions. Arduino board permits developing solutions like that. This article presents the academic experience of first-year students of Ingeniería de Sistemas e Informática at the Universidad Continental (ISI-UC) of Huancayo, Perú, using the Arduino microcontroller board for the teaching-learning process to develop programming competencies. The results obtained show a positive impact regarding the previous year’s experience using traditional text-based programming languages. Using Arduino, students create digital circuits and computational electronics competencies, another significant benefit. This experience used an online simulator, and the results obtained permit us to plan future online education strategies for this major. Next step will be the application of Arduino and the online simulator to deepen the programming competencies including recursivity, real time constraints , multitasking features, data structure and object-oriented programming. The main limitations encountered in this experiment were the student limitations regarding electronics concepts in order to build up circuits and in in some cases low internet speeds to attend online education. Performing simulated experiences in classes were not a big challenge for teachers and most students. Nevertheless, problems could appear when performing practical experiences using real Arduino boards, which would may not be available in traditional classes.

Learning computer programming is a challenging process. Among the current approaches for overcoming this challenge, visual programming languages (VPLs), like Scratch, have shown very promising results for beginners. Interestingly, some higher-education institutions have started to use VPLs to introduce basic programming concepts, mainly in CS1 courses. However, an important issue regarding Scratch?s usage in higher education environments is that students may feel unmotivated being confronted by programming exercises that do not fulfill their individual expectations. To try and overcome this barrier, we propose CARAMBA, a Scratch extension including an exercise recommender system. Based on features like \textit{taste} and \textit{complexity}, CARAMBA is able to personalize student learning with Scratch by suitably suggesting exercises for students. An in-depth evaluation was conducted about the effects of our proposal on both the learning of basic concepts of CS1 and the overall performance of students. We adopted an equivalent pretest-posttest design with 88 college students at an Ecuadorian university. Results confirm that recommending exercises in Scratch had a positive effect on students programming learning abilities in terms of success rates. In totality, the success rate achieved by our proposal was over 52%, which is 8% higher than the rate achieved during a previous experience using only Scratch (without recommendation) and 21% higher than the historical results of traditional teaching (without Scratch). Furthermore, we analyzed the degree of exploitation of CARAMBA by students to portray two facts: students actually used CARAMBA and there was a positive correlation between the utilization of CARAMBA and the scores obtained by the students.

Teaching computer programming is a real challenge in the State University of Milagro (UNEMI), located in one of the least-developed zones in Ecuador, a non-WEIRD country (WEIRD stands for Western, Educated, Industrialized, Rich and Democratic). Despite the application of various learning strategies, the historical pass rate does not exceed 43%. To solve this problem, we have relied on visual programming languages, specifically Scratch. Scratch is an open source software to learn programming that has a strong assumption of the benefits of community work. A quasi-experiment conducted with 74 undergraduate students during the first semester of CS showed that: (1) Both groups (control and experimental) are homogeneous in terms of their demographic characteristics, previous academic performance and motivation (expectations) concerning the course; (2) Scratch is strongly accepted by students in the experimental group and concerning the learning process, both groups showed similar levels of satisfaction; (3) the experimental group showed a pass rate four times higher than the control group; (4) in general, student success is associated with having learned programming with Scratch. While limited, our results are an important step in our road to improve the learning of programming in a low social status area of Ecuador.

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

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.

Este trabajo presta especial atención al concepto de Next Generation Digital Learning Environment (Entorno Educativo Digital de Próxima Generación, NGDLE) y proporciona una lista de componentes típicos que se pueden integrar en el entorno educativo de acuerdo con el modelo NGDLE. Consideramos la posibilidad de sumergir la teoría del socio-constructivismo de Lev Vygotsky en un entorno digital. El significado de la teoría sociocultural del desarrollo cognitivo consiste en el cambio sucesivo de zonas de desarrollo de acuerdo con el esquema "zona de desarrollo real⇒ zona de desarrollo próximo⇒ zona de desarrollo posterior". Para este propósito se plantea la tarea de construir un modelo matemático de la zona de desarrollo próximo, basado en la teoría de medición de rasgos latentes y psicometría computacional. Para comparar el ancho de la zona del desarrollo próximo del valor numérico, utilizamos la idea de su proporcionalidad al nivel de capacitación, que a su vez se considera como una variable latente. Para una descripción formal de este indicador, se propone una lista de indicadores de medición: indicadores de actividad educativa y el patrón de interacción del usuario con el entorno digital. El modelo construido de la zona de desarrollo se utiliza para la gestión adaptativa del E-learning, el cual presenta un algoritmo de gestión adaptativa que implementa el aprendizaje del desarrollo. Se describe la técnica de evaluar la conveniencia de aplicar el modelo de la zona de desarrollo proximal. Para evaluar la viabilidad, se utiliza material experimental: datos sobre los resultados de las actividades educativas de los sujetos. Como resultado del estudio, se confirmó la hipótesis de que el nivel de preparación alcanzado en el curso del desarrollo de la educación es mayor que los resultados del aprendizaje electrónico, sin tener en cuenta la teoría del desarrollo de la educación. Además, se consideran enfoques de la arquitectura del moderno entorno educativo digital.