Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE.

In this paper, we describe an educational experience in the context of the Master’s degree that is compulsory in Spain to become a secondary education mathematics teacher. Master’s students from two universities in Madrid (Spain) attended lectures that addressed—emphasizing the concourse of a dynamic geometry software package—some historical, didactic and mathematical issues related to linkage mechanisms, such as those arising in the 18th and 19th centuries during the development of the steam engine. Afterwards, participants were asked to provide three different kinds of feedback: (i) working on an assigned group task, (ii) individually answering a questionnaire, and (iii) proposing some classroom activity, imagining it would be addressed to their prospective pupils. All three issues focused on the specific topic of the attended lectures. In the framework of Mason’s reflective discourse analysis, the information supplied by the participants has been analyzed. The objective was to explore what they have learned from the experience and what their perception is of the potential interest in linkages as a methodological instrument for their future professional activity as teachers. This analysis is then the basis upon which to reflect on the opportunities (and problems) that this particular bar-joint linkages methodological approach could bring towards providing future mathematics teachers with attractive tools that would contribute to enhancing a STEAM-oriented education. Finally, the students’ answers allow us to conclude that the experience was beneficial for these pre-service teachers, both in improving their knowledge on linkages history, mathematics, industrial, technological and artistic applications, and in enhancing the use in the classroom of this very suitable STEAM context.

The development and deployment of the so-called Industrial Internet of Things (IIoT) have significantly increased the control and monitoring capabilities of companies, and thus their potential productivity. In this paper, we propose the use of Raspberry Pi devices in industrial environments to mea-sure productivity parameters. Our proposal can economically and efficiently gather data related with the availability and productivity of industrial machinery. However, since low-cost devices are prone to suffer the negative effects of electromagnetic interferences, we additionally propose an alternative to prevent signal alterations caused by them. More specifically, we propose a filtering mechanism called Smart Coded Filter (SCF), which eliminates wrong signals caused by electromagnetic interferences, and, therefore, highly improves the accuracy when estimating the availability metric. Results obtained demonstrate that our low-cost device provided with the SCF completely ignores 100% of wrong availability data, while reducing up to 70% the number of records stored into the database.

Esta investigación de carácter internacional tiene un doble objetivo: en primer lugar, identificar perfiles de directores escolares de educación secundaria según sus percepciones sobre el impacto de la inteligencia artificial generativa en el aprendizaje del alumnado; y, en segundo lugar, determinar si existen diferencias en estos perfiles entre países. Para ello, se analizan los microdatos del Módulo de IA de la evaluación internacional ICILS 2023, con una muestra final de 968 directores escolares de diez países. Se emplea un Análisis de Perfiles Latentes ponderado para diseño complejo, que permite identificar patrones diferenciados en las percepciones y actitudes de los directores frente a la IA en contextos educativos. Los resultados evidencian tres perfiles claramente diferenciados: Directores indiferentes (1,42 %), Directores ambivalentes (3,18 %) y Directores con posiciones intermedias (95,4 %), con algunas variaciones entre países. Estos hallazgos sugieren que, hasta el momento, no existe una opinión sólidamente formada sobre el potencial de la IA para transformar el aprendizaje estudiantil. Por ello, las decisiones y estrategias que se adopten en el futuro próximo serán determinantes para que la IA se convierta en una herramienta educativa esencial o, por el contrario, en un obstáculo para el aprendizaje de los estudiantes.

This study addresses an implementation problem for informatics education: whether teachers’ reported participation in AI-related professional learning, interpreted as realised access to one professional learning condition for teacher AI literacy, is associated with declared need or instead follows existing patterns of digital, professional and organisational advantage. The study does not measure teacher AI literacy, AI competence, computing teaching practice, computational-thinking instruction or classroom implementation directly. Rather, it analyses reported participation in AI-related professional learning as a realised opportunity condition for developing teacher AI literacy at scale. Using TALIS 2024 data from 108,136 lower-secondary teachers nested within 10,840 schools across 55 education systems, three-level multilevel linear probability models and random slopes at the education-system level were estimated. Results showed substantial cross-system inequality in reported participation. Variance decomposition located 8.5% of total variation at the education-system level and 9.7% at the school level. Declared need was only partially associated with reported participation: teachers reporting low or moderate need were more likely to have participated in AI-related professional learning than those reporting no need, whereas teachers with the highest need showed no significant advantage. Digital self-efficacy and professional collaboration were consistently associated with higher participation. At the school level, digital resource shortages and school digital leadership support were significant predictors. Random-slope estimates showed that the association between high declared need and participation varied significantly across education systems. The findings suggest that equitable teacher AI literacy requires deliberate opportunity structures, not only competence frameworks or voluntary participation in professional learning.

Proprietary systems used to modernize Industry 4.0 usually involve high financial costs. Consequently, using low-cost devices with the same functionalities, capable of replacing these proprietary systems but at a lower cost, has become an incipient trend. However, these low-cost devices usually come with electromagnetic interference problems as they are encapsulated in electrical panels, sitting alongside electromechanical devices. In this article, we present Mode Binary Search, an algorithm specifically designed for use in a low-cost automated-industrial-productivity-data-collection system. Specifically, productivity data are obtained from the availability and sealing signals of the thermoplastic sealing machines in production lines belonging to the agri-food industry. Mode Binary Search was designed to cluster sealing signals, thus enabling us to identify which products have been made. Furthermore, the algorithm determines when the manufacturing of each product starts and ends, in other words, the exact moment a product change occurs and all this without the need for operator supervision or intervention. Finally, we compared our algorithm, based on binary search, with three clustering mechanisms: k-means, k-rms and x-means. Out of all the cases we analyzed, the maximum error committed by Mode Binary Search is limited to 2.69%, thereby outperforming all others.