Search Results (147)

This article describes the design and implementation of a Specialized Mental Health Clinical Pastoral Education (CPE) program for clergy and theological students of all spiritual, religious, and cultural backgrounds. Addressing the need for mental health competencies in religious leaders and chaplains, this training equips participants with specialized skills in individual and group mental health spiritual assessment and spiritual care. Program participants become effective members of a multiprofessional team, gain knowledge of mental health philosophies of concordant and discordant spiritual orienting systems, and gain greater relational capacity. The Mental Health CPE Program includes traditional and novel CPE elements: (1) clinical practice, (2) group and individual supervision, (3) didactic presentations and journal clubs, (4) verbatim (Protocols of patient encounters) and case presentations, (5) group leadership training and practica, and (6) experience of faith reflections. This article provides insights into the origins of CPE, a description of the implementation in a free-standing psychiatric hospital, and observed developmental changes of program participants. We include graduates’ anecdotal feedback about their learning experience and its impact on their leadership in chaplaincy and in religious communities. Additionally, we report on areas for future development and further study of the effectiveness of Mental Health CPE. Full article

This paper outlines a methodology for creating an educational and informative communication system for non-specialised audiences in order to preserve and pass on the heritage of ideas and practices adopted in the medieval political and administrative sphere. Through the combined use of digital technologies (such as GISs, 3D modelling and virtual tours), historical sources can potentially reveal how political and administrative aspects affected different areas within the medieval city, not just the main seats of power. Bologna, a prestigious medieval university metropolis, is chosen as a case study because of the remarkable wealth of documentation in its archives from the city’s political culture in the Middle Ages. Written historical sources, including documentary and narrative texts, are among the primary tools employed in the study of European medieval urban communities in general. Documentary sources help us understand and reconstruct the complexities of civic administration, urban policies and the economy, as well as how citizens experience them daily. The involvement of citizens in the political and administrative life of late medieval cities is explored through the management and digital processing of historical documentation. Digital humanities tools can facilitate this analysis, offering a perspective that sheds light on the formation of the pre-modern state. Although digital databases and repositories have significantly contributed to preserving and digitally archiving historical sources, these are often aimed exclusively at the academic level and remain underutilised as privileged didactic and educational tools for a broad audience. Full article

Fractional modeling has emerged as an important resource for describing complex phenomena and systems exhibiting non-local behavior or memory effects, finding increasing application in several areas in physics and engineering. This study presents the analytical derivation of equations pertinent to the modeling of different systems, with a focus on heat conduction. Two specific boundary value problems are addressed: a Helmholtz equation modified with a fractional derivative term, and a fractional formulation of the Laplace equation applied to steady-state heat conduction in circular geometry. The methodology combines the separation of variables technique with fractional power series expansions, primarily utilizing the Caputo fractional derivative. An important aspect of this paper is its instructional emphasis, wherein the mathematical derivations are presented with detail and clarity. This didactic approach is intended to make the analytical methodology transparent and more understandable, thereby facilitating greater comprehension of the application of these established methods to non-integer-order systems. The final goal is not only to provide a different approach of solving these physical models analytically, but to provide a clear, guided pathway for those engaging in the treatment of fractional differential equations. Full article

The article investigates a learning setting contextualising the use of artificial intelligence in heritage education, with a particular focus on AI systems utilising text-to-image processes. The setting is the one of a university interdisciplinary seminar in communication in South Tyrol, a border region in the north of Italy shaped by a strong cultural identity. The paper illustrates a didactic experience introducing a highly technical and, for most of the students in the chosen context, challenging topic, such as AI. The teaching addresses a critical approach to AI, such as dataset constraints, sustainability, and authorship, and focuses on text-to-image algorithms and artistic co-creation, namely, the shifting role of the artist from sole creator to initiator/collaborator shaping the AI system’s output. The aim of the paper is to contribute to the debate in heritage education on teaching and learning using AI-based systems. The latter are seen as a potential tool for the engagement of students in understanding heritage and its safeguarding and in the relationship between community, territory, and active participation, as emphasised by both the “UNESCO Convention on Intangible Cultural Heritage” and the “Council of Europe Framework Convention on the Value of Cultural Heritage for Society”. However, the current boundaries of AI, particularly in terms of bias and limitations of datasets, must be addressed and reflected on. Full article

While pharmacy education successfully employs various methodologies including case-based learning and simulated patient interactions, providing consistent, individualized guidance at scale remains challenging in team-based learning environments. Artificial intelligence (AI) offers potential solutions through automated facilitation, but its possible utility in pharmacy education remains unexplored. We developed and evaluated an AI-guided patient case discussion simulation to enhance learners’ ability to integrate foundational science knowledge with clinical decision-making in a didactic immunology course in a US PharmD program. We utilized a large language model programmed with specific educational protocols and rubrics. Here, we present the step-by-step prompt engineering protocol as a toolkit. The system was structured around three core components in an immunology team-based learning activity: (1) symptomatology analysis, (2) laboratory test interpretation, and (3) pharmacist role definition and PPCP. Performance evaluation was conducted using a comprehensive rubric assessing multiple clinical reasoning and pharmaceutical knowledge domains. The standardized evaluation rubric showed reliable assessment across key competencies including condition identification (30% weighting), laboratory test interpretation (40% weighting), and pharmacist role understanding (30% weighting). Our AI patient simulator offers a scalable solution for standardizing clinical case discussions while maintaining individualized learning experiences. Full article

In recent years, the educational field has evolved rapidly owing to the integration of several technologies, especially experiments in remote laboratories in the engineering area. Therefore, this article addresses the development of an innovation system for automatically correcting experiments in remote laboratories in mechatronics using digital twins, convolutional neural networks (CNNs), and generative artificial intelligence technologies. This system was designed to overcome the limitations of physical laboratories and teacher’s availability and assist in learning, enabling automatic acquisitions at any time. The digital twin captures data from the teacher’s and student’s experiments, allowing accurate comparisons to identify successes and errors. The application of CNNs serves to validate the results of the experiments through image analysis, whereas generative AI helps to identify patterns. The system was evaluated in a didactic plant, effectively correcting experiments with digital inputs and outputs. In addition, it provides students with detailed feedback on their performance, including specific errors and suggestions for improvement. With a three-layer architecture, i.e., experiments, didactics, and management, the system efficiently processes data from teachers and students, contributing to correcting experiments and optimizing teaching in remote environments. Full article

The growth in the number of students in higher education institutions (HEIs) in Latin America reached 33.5 million in 2021 and more than 220 million worldwide, increasing the number of data volumes in academic management systems. Some of the difficulties that universities face are providing high-quality education to students and developing systems to evaluate the performance of teachers, which encourages offering a better quality of teaching in universities; in this sense, machine learning emerges with great potential in education. This literature review aims to analyze the factors, machine learning algorithms, challenges, and limitations most used to evaluate the quality of teaching based on performance. The methodology used is PRISMA, which considers analyzing literature produced between 2014 and 2024 on factors, prediction algorithms, challenges, and limitations to predict the quality of teaching. Here, 54 articles from journals indexed in the Web of Science and Scopus databases were selected, and 111 factors were identified and categorized into five dimensions: teacher attitude, teaching method, didactic content, teaching effect, and teacher achievements. Regarding the advances in machine learning in predicting teacher teaching quality, 30 ML algorithms were identified, the most used being the Back Propagation (BP) neural network and support vector machines (SVM). The challenges and limitations identified in 14 studies related to HEIs are managing the large volume of data and how to use it to improve the quality of education. Full article

This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor’s response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising $6.99\%$ error percentage. Full article

Background/Objectives: The educational system thinking approach (ST) takes a holistic vision of instructors/teachers and learners’ relationships, making sports pivotal for reflection on education. This study evaluated the efficacy of a multisport ST-based course on minirugby instructors’ teaching competence and children players’ motor conduct. Methods: The twenty-five rugby instructors (IAC) attended the 25 h course and the children of their teams (n = 109, Ch-IAC) participated in this study as experimental groups. Twenty-five rugby instructors who were not attending the course (I-CON) and their pupils (n = 111, Ch-CON) acted as control groups. Changes in instructors’ teaching competence (by the Instrument for Identifying the Teaching Style and the System for Observing Fitness Instruction Time) and children’s motor conduct (by the Körperkoordinationtest für Kinder test, the Game Performance Assessment Instrument, the Physical Activity Enjoyment Scale, and the physical self-efficacy scale for children) were assessed. Results: Thanks to the education received, IAC improved in didactics and methodological competence. They learned to use more production teaching styles than CON (10.5 ± 9.3% vs. 0% of the lesson time, p < 0.05), reduce children’s inactive lesson time for management (−5.1 ± 3.3% vs. 1.1 ± 3.1%, p < 0.05) and promote more outside lesson topics (4.4 ± 3.2% vs. 0%, p < 0.05). In addition, compared to Ch-CON, Ch-IAC significantly improved motor coordination, game performance, enjoyment, and self-efficacy (p < 0.05). Conclusions: Children’s enjoyment and self-efficacy acted as mediators that amplified the effects of the multisport training course. At the same time, the instructor’s didactical and methodological competence were moderators directly favoring or worsening children’s motor competence. Such an integrated multisport model is applicable and suggested for improving sports performance and education processes. Full article

Background/Objectives: The concept of educational systems thinking shows the rugby educational system as a network of multiple interactive and interconnected elements. However, the frameworks presented in the literature for developing sports practice through an educational vision and multisport engagement do not always have direct transferability to instructors’ training courses. The study aims to evaluate the correlation between rugby instructors’ methodological and didactical competencies and compare them with children’s motor competence and psychological aspects. Methods: Two hundred twenty children (9.6 ± 1.1 years) and 50 instructors (39.0 ± 13.6 years) were enrolled. Instructors’ competencies were investigated during the internship through evaluation sheets, questionnaires, and video analysis. Children’s motor competence and psychological aspects were investigated through motor tests, questionnaires, and video analysis. Results: The correlation between methodological competencies and didactical-communicative competencies showed a positive significance, especially regarding production styles. Motor competence and play skills in children showed poor results. The group of instructors with more methodological competencies (a greater number of teaching styles used) showed a strong correlation between motor competence and psychological aspects. Conclusions: For successful teaching, knowledge of specific technical skills is not enough, and it is important to consider all the factors (in particular, the teaching-communicative and methodological skills) that contribute most to instructors’ skills. Full article

Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed. Full article

The COVID-19 pandemic led to the widespread closure of educational systems, prompting a shift to online learning methods for instructors and students in higher education institutions. This study explores how pedagogical instructors supported accessibility to online pedagogical training during this crisis. This qualitative investigation employed a case study methodology, focusing on the high school training program at a multicultural college of education in Israel. Semi-structured interviews were conducted with six pedagogical instructors to explore their roles as educators, their oversight of pedagogical training, the implementation of didactic workshops and practical work, and their responses to the COVID-19 pandemic across technological, pedagogical, and emotional dimensions. Findings underscore the critical role of pedagogical instructors during the shift to remote learning. They facilitated communication using digital tools among pre-service teachers, coaching professors, and students, akin to conducting an orchestra with multiple participants. Amid widening educational, technical, and social disparities among students, pedagogical instructors and their pre-service teachers played a pivotal role in curriculum adaptation and crisis management within the school setting. Full article

The One Health approach often reaches classrooms through Environmental Education (EE), which aims to guide society’s response to current crises (environmental, health, economic, values). However, traditional EE teaching has focused on isolated ecological concepts and standardised solutions, ignoring the multidimensional nature of EE and failing to integrate the interdependence between environmental, animal, and human health. Moreover, teacher training often neglects didactic content knowledge, preventing students from acquiring the systemic vision needed to address eco-social problems and create sustainable solutions aligned with the Sustainable Development Goals (SDGs). This limits teachers’ ability to influence students’ concerns and behaviours. In this context, this study aims to reflect on the current state of the issue and propose strategies informed by Science Education research to improve EE teaching, enabling the integration of One Health dimensions through effective didactics to achieve Transformative Environmental Education (TEE). For this purpose, we begin by addressing the limitations identified in recent systematic reviews, shifting the paradigm towards a symbiosis of EE and Science Education through scientific practices. We then present practical examples showcasing successful EE initiatives that foster a deeper understanding of socio-environmental issues, encourage innovative solutions, and nurture engaged citizens from early education onwards. These proposals can support classroom practice and ongoing teacher self-development. Pedagogical strategies include tackling issues that require systemic and critical thinking by developing scientific and epistemic practices while raising awareness of Environmental Justice. Thus, this study advocates for a new vision of EE, integrating the One Health approach, which could be applied to develop new educational programs, including teacher training. This would lead to a new learning evaluation model and help identify key determinants that trigger pro-environmental behaviours. Full article

This article explores the integration of a hapto-visual digital twin on a master device used for bilateral teleoperation. The device, known as a quasi-spherical parallel manipulator, is currently employed for the remote center of motion control in teleoperated mini-invasive surgery. After providing detailed insights into the device’s kinematics, including its geometric configuration, Jacobian, and reachable workspace, the paper illustrates the overall control system, encompassing both hardware and software components. The article describes how a digital twin, which implements a haptic assistive control and a visually enhanced representation of the device, was integrated into the system. The digital twin was then tested with the device: in the experiments, one “student” end-user must follow a predefined “teacher” trajectory. Preliminary results demonstrate how the overall system can pose a good starting point for didactic telesurgery operation. The control action, yet to be optimized and tested on different subjects, indeed seems to grant satisfying performance and accuracy. Full article

The identification (and control) of nonlinear systems is one of the most important and actual research directions. Moreover, many systems are multivariable. Different from linear system identification (where only a few classes of models are available), in the case of nonlinear systems, the class set of models is quite diverse. One of the most appealing nonlinear models belongs to the nonlinear ARMAX (NARMAX) class. This article focusses on the identification of such a model, which can be compared with other models (such as nonlinear ARX (NARX) and linear ARMAX) in an application based on the didactical installation ASTANK2. The mathematical foundation of NARMAX models and their identification method are described at length within this article. One of the most interesting parts is concerned with the identification of optimal models not only in terms of numerical parameters but also as structure. A metaheuristic (namely, the Cuckoo Search Algorithm) is employed with the aim of finding the optimal structural indices based on a special cost function, referred to as fitness. In the end, the performances of all three models (NARMAX, NARX, and ARMAX) are compared after the identification of the ASTANK2 installation. Full article