As a large Open University system, Universitas Terbuka (UT) has consistently implemented an effective performance management and reward system to ensure that performance is objectively measured, improvement can be effectively made, and people are fairly rewarded based on performance. This paper addresses the influence of emotional intelligence on management performance in an open and distance learning (ODL) institution. Emotional intelligence is defined as managing feelings so that they are expressed appropriately and effectively, enabling people to work together smoothly toward their common goals. This study aims to investigate the influence of emotional intelligence of top management staff in the 37 UT Regional Offices, in Indonesia. Besides UT’s headquarters in Jakarta, as an ODL institution, UT also has 37 Regional Offices all over Indonesia that are responsible for students’ academic and non-academic services. The participants were therefore all the 37 Heads of Regional Offices of UT. The data was obtained in two ways, namely primary data about emotional intelligence obtained through the administration of a questionnaire and secondary data about performance obtained through the office of the Vice Rector III. The results of the study showed that four elements of emotional intelligence (self-awareness, self-regulation, motivation, and social skills) strongly influenced management performance. One element of emotional intelligence, namely empathy, did not significantly affect management performance. Overall, the results suggested that it is important for an ODL institution to have leaders and employees with high emotional intelligence to achieve the goal effectively.
Background:
The aging population in India is steadily increasing, with projections estimating a rise of approximately 56 million elderly individuals by 2031.[1] The concept of life satisfaction is crucial in understanding the well-being of older adults, with various factors such as income level, health status, and social connections playing pivotal roles. Positive social relationships and support have been shown to promote life satisfaction and mitigate the risk of depression among the elderly. This study explores the relationship between social networks and life satisfaction among the geriatric population in Tamil Nadu, India.
Materials and Methods:
A cross-sectional study involving 403 elderly individuals aged 60 years and above was conducted in the field practice area of a tertiary care hospital in Tamil Nadu. Participants were selected using a multistage simple random sampling method, and data were collected through interviews and surveys. The study used the Lubben Social Network Scale (LSNS) and the Satisfaction With Life Scale (SWLS) to assess social networks and life satisfaction, respectively. Data analysis was performed using SPSS software, with descriptive statistics and Chi-square tests employed for analysis.
Results:
Among the total participants, 67% were found to have a decent social network, while the remaining 33% had poor social networks. Decent Family, friend, and neighbor networks were observed in this demographic. Individuals with strong social networks reported higher levels of satisfaction.
Conclusion:
Social networking positively influences life satisfaction. Strengthening social networks and support systems can significantly contribute to promoting the well-being and quality of life of the geriatric population.
Special aspects of education, Public aspects of medicine
Wonoasri Tempurejo Jember merupakan desa yang berbatasan langsung dengan Taman Nasional Meru Betiri, hutan lindung terbesar di Jawa Timur. Di Desa Wonoasri terdapat Koperasi Usaha Bersama (KUBE) bernama Meru Betiri yang beranggotakan 15 orang pembatik. Setiap bulan pengrajin batik menghasilkan 30 potong kain batik tulis pewarna alam dengan proses pengerjaan kain batik 3-7 hari per lembar kain batik. Penjualan batik tulis ini sangat rendah karena harga yang mahal sehingga pendapatan pengrajin batiknyapun rendah. Pengabdian kepada masyarakat desa Wonoasri ini bertujuan untuk meningkatkan penjualan produk kain berpewarn alam dengan memanfaatkan potensi hayati desa Wonoasri. Pelaksanaan pengabdian ini dilakukan dengan pelatihan dan pendampingan pembuatan kain dengan pewarna alam dengan teknik ecoprint basic mirroring dan blanket. Dalam sosialisasi ini diajarkan untuk menentukan jenis bahan kain, memilih bahan alam diantaranya, daun yang mengandung tanin tinggi, bahan mordan, persiapan mordan kain, membuat pewarna alami, menata daun, dan proses pengukusan kain. Pelatihan dan pendampingan ini dapat meningkatkan keterampilan peserta untuk membuat kain ecoprint yang berkualitas hingga 100%. Adanya varian produk kain pewarna alam diharapkan dapat meningkatkan pendapatan pembatik desa Wonoasri karena kain ecoprint memiliki harga jual terjangkau dan proses pembuatan menggunakan tumbuhan yang ada di desa Wonoasri sehingga mampu mengurangi biaya pembeliaan bahan baku. Dengan produk yang bermutu tinggi perlu juga ditunjang dengan pengetahuan digital marketing untuk media promosi. Pelatihan pengenalan digital marketing telah dilaksanakan, dan masih perlu pendampingan untuk melakukan promosi produk melalui sosial media Instagram maupun menggunakan google my business.
This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework, focusing on consistency, authenticity, and traceability. We design a five-layer architecture that includes prompt-driven synthetic dialogue generation, a structured variable system covering skills, institutional, and emotional dimensions, dependency and causal path modeling, graph-based structure design, and an interactive inference engine. Empirical results demonstrate the effectiveness of the approach using a 15-segment synthetic corpus, with 41,597 tokens, 127 annotated variables, and 820 semantic relationship triples. The model exhibits strong structural consistency (Krippendorff alpha = 0.83), construct validity (RMSEA = 0.048, CFI = 0.93), and semantic alignment (mean cosine similarity > 0.78 via BERT). A key causal loop is identified: system mismatch leads to emotional frustration, reduced participation, skill gaps, and recurrence of mismatch, revealing a structural degradation cycle. This research introduces the first NIST-compliant AI modeling framework for stakeholder systems and provides a foundation for policy simulation, curriculum design, and collaborative strategy modeling.
Welington Fabrício dos Santos Costa, Jesuíno Alves Martins Júnior, André Flávio Gonçalves Silva
et al.
In recent years, the frequency of extreme weather events on Earth has increased significantly. This phenomenon is driven by the intensification of the greenhouse effect caused by anthropogenic activities, leading to temperature variations in urban environments that affect thermal comfort and quality of life. Given this context, the present study investigates temperature mapping in urban biomes using an infrared thermometer, conducted as part of a hands-on workshop offered during the 21st National Science and Technology Week. The initiative involved students from the public school system and was grounded in Physics education, aiming to foster scientific enculturation. Participants engaged in a problem-based learning experience, actively contributing to all stages of the knowledge construction process. The objective was to examine the relationship between vegetation presence and its impact on temperature in urban environments. A qualitative and quantitative methodological approach was adopted, enabling the identification of scientific literacy indicators such as information sequencing, data organization, logical reasoning, hypothesis formulation, justification, and explanation of observed phenomena. The analysis of students' statements and activity guidelines provided insights into their critical thinking development. The findings indicate that students developed essential skills for understanding physical and environmental phenomena, effectively linking collected data to scientific concepts and proposing well-supported interpretations. Moreover, the experience reinforced the perception of science as a dynamic and investigative process, fostering curiosity and enhancing students' argumentative abilities. Thus, the workshop proved to be an effective strategy for promoting scientific literacy and engaging participants in the study of environmental impacts in urban contexts.
As Artificial Intelligence (AI), particularly Large Language Models (LLMs), becomes increasingly embedded in education systems worldwide, ensuring their ethical, legal, and contextually appropriate deployment has become a critical policy concern. This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions, including the European Union, United Kingdom, United States, China, and Gulf Cooperation Council (GCC) countries. It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures. Special emphasis is placed on the evolving landscape in the GCC, where countries are rapidly advancing national AI strategies and education-sector innovation. To support this development, the paper introduces a Compliance-Centered AI Governance Framework tailored to the GCC context. This includes a tiered typology and institutional checklist designed to help regulators, educators, and developers align AI adoption with both international norms and local values. By synthesizing global best practices with region-specific challenges, the paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education. These insights are intended to inform future regulatory harmonization and promote responsible AI integration across diverse educational environments.
Introducción: el presente artículo se realizó a partir de detectar insuficiencias teórico-metodológicas y prácticas en el proceso de preparación técnica de la natación artística escolar.
Objetivo: elaborar una concepción teórica con una nueva forma de reestructurar el contenido para el entrenamiento de las figuras, sobre la base de su periodización, que favorezca el incremento del rendimiento deportivo en nadadores artísticos categoría escolar.
Materiales y métodos: se emplearon como métodos teóricos de la investigación científica el analítico-sintético y el sistémico estructural funcional. De los empíricos, el análisis documental y el criterio de expertos. Como métodos matemático-estadísticos, la estadística descriptiva e inferencial, con la Prueba de W de Kendall.
Resultados: la calidad de la concepción teórica quedó corroborada por la evaluación emitida por los 15 expertos seleccionados, que tuvieran relación directa ya sea con el entrenamiento deportivo o de modo particular en el entrenamiento de la natación artística cubana.
Conclusiones: los expertos en la evaluación de los indicadores, resaltan su funcionabilidad, pertinencia y factibilidad.
Kristin A. Oliver, Victoria Borish, Bethany R. Wilcox
et al.
As quantum technologies transition out of the research lab and into commercial applications, it becomes important to better prepare students to enter this new and evolving workforce. To work towards this goal of preparing physics students for a career in the quantum industry, a senior capstone course called "Quantum Forge" was created at the University of Colorado Boulder. This course aims to provide students a hands-on quantum experience and prepare them to enter the quantum workforce directly after their undergraduate studies. Some of the course's goals are to have students understand what comprises the quantum industry and have them feel confident they could enter the industry if desired. To understand to what extent these goals are achieved, we followed the first cohort of Quantum Forge students through their year in the course in order to understand their perceptions of the quantum industry including what it is, whether they feel that they could be successful in it, and whether or not they want to participate in it. The results of this work can assist educators in optimizing the design of future quantum-industry-focused courses and programs to better prepare students to be a part of this burgeoning industry.
This paper explores the causal impact of education opportunities on rural areas by exploiting the higher education expansion (HEE) in China in 1999. By utilizing the detailed census data, the cohort-based difference-in-differences design indicates that the HEE increased college attendance and encouraged more people to attend senior high schools and that the effect is more significant in rural areas. Then we apply a similar approach to a novel panel data set of rural villages and households to examine the effect of education opportunities on rural areas. We find contrasting impacts on income and life quality between villages and households. Villages in provinces with higher HEE magnitudes underwent a drop in the average income and worse living facilities. On the contrary, households sending out migrants after the HEE experienced an increase in their per capita income. The phenomenon where villages experienced a ``brain drain'' and households with migrants gained after the HEE is explained by the fact that education could serve as a way to overcome the barrier of rural-urban migration. Our findings highlight the opposed impacts of education opportunities on rural development and household welfare in rural areas.
Ikpe Justice Akpan, Yawo M. Kobara, Josiah Owolabi
et al.
Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human-like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human-chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n=75) occurred during 2006-2018, while 2019-2023 experienced astronomical growth (n=1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science [multidisciplinary and AI] (32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, Information Systems, and other areas. The thematic structure of SLP highlights prominent CGAI use cases, including improved user experience in human-computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self-diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline-based automatic detection of GenAI contents to check abuse is proposed.
Transitioning from Education 1.0 to Education 5.0, the integration of generative artificial intelligence (GenAI) revolutionizes the learning environment by fostering enhanced human-machine collaboration, enabling personalized, adaptive and experiential learning, and preparing students with the skills and adaptability needed for the future workforce. Our understanding of academic integrity and the scholarship of teaching, learning, and research has been revolutionised by GenAI. Schools and universities around the world are experimenting and exploring the integration of GenAI in their education systems (like, curriculum design, teaching process and assessments, administrative tasks, results generation and so on). The findings of the literature study demonstrate how well GenAI has been incorporated into the global educational system. This study explains the roles of GenAI in the schooling and university education systems with respect to the different stakeholders (students, teachers, researchers etc,). It highlights the current challenges of integrating Generative AI into the education system and outlines future directions for leveraging GenAI to enhance educational practices.
Research into educational physics is a field of study that has undergone sustained growth in recent decades. Among the topics addressed in educational physics, there is a relatively new field of research that seeks to make advanced physics accessible to teachers in this area, that is, physics that, due to its novelty and complexity, is beyond the reach of a typical high school teacher. For the reasons discussed in this paper, I the name advanced physics transposition or APT is suggested for this field of research. The objective of this work is to make a first attempt to systematise this field, to draw attention to the place it occupies, and no less important to give it a name that represents the objectives, methods and results that characterise it.
Grading assessments is time-consuming and prone to human bias. Students may experience delays in receiving feedback that may not be tailored to their expectations or needs. Harnessing AI in education can be effective for grading undergraduate physics problems, enhancing the efficiency of undergraduate-level physics learning and teaching, and helping students understand concepts with the help of a constantly available tutor. This report devises a simple empirical procedure to investigate and quantify how well large language model (LLM) based AI chatbots can grade solutions to undergraduate physics problems in Classical Mechanics, Electromagnetic Theory and Quantum Mechanics, comparing humans against AI grading. The following LLMs were tested: Gemini 1.5 Pro, GPT-4, GPT-4o and Claude 3.5 Sonnet. The results show AI grading is prone to mathematical errors and hallucinations, which render it less effective than human grading, but when given a mark scheme, there is substantial improvement in grading quality, which becomes closer to the level of human performance - promising for future AI implementation. Evidence indicates that the grading ability of LLM is correlated with its problem-solving ability. Through unsupervised clustering, it is shown that Classical Mechanics problems may be graded differently from other topics. The method developed can be applied to investigate AI grading performance in other STEM fields.
Dawn Foster-Hartnett, Gwantwa Mwakalundwa, Greta Henry
et al.
ABSTRACT We developed a course-based undergraduate research experience (CURE) that gives students an opportunity to practice the process of science in a context that intersects with their everyday lives: purchasing grocery store chicken. Student mastery of concepts was assessed by pre- and postassessment questions and lab report worksheets that guided them through the process of writing a scientific paper. Learning to produce graphs from large data sets and comparing the results with published data emphasized quantitative reasoning, while working as a group and writing helped students practice scientific communication. Most students (>90%) met the learning objectives, and students in both groups reported feeling more confident producing graphs and figures; they also showed large gains in confidence and interest in bioinformatics. Lab protocols require biosafety level 2 safety guidelines; however, students in an online or dry lab setting can use the compiled data sets and whole-genome sequences to complete the objectives. Group discussions and essay prompts at the end encourage students to use evidence-based arguments to make decisions that impact the global issue of antimicrobial resistance.
Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles students encounter throughout their learning journey. Solving the problems encountered by students poses a significant challenge for traditional deep learning models, as it requires not only a broad spectrum of subject knowledge but also the ability to understand what constitutes a student's individual difficulties. It's challenging for traditional machine learning models, as they lack the capacity to comprehend students' personalized needs. Recently, the emergence of large language models (LLMs) offers the possibility for resolving this issue by comprehending individual requests. Although LLMs have been successful in various fields, creating an LLM-based education system is still challenging for the wide range of educational skills required. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering, with the aim to explore their potential in constructing the next-generation intelligent education system. Specifically, for each capability, we focus on investigating two aspects. Firstly, we examine the current state of LLMs regarding this capability: how advanced they have become, whether they surpass human abilities, and what deficiencies might exist. Secondly, we evaluate whether the development methods for LLMs in this area are generalizable, that is, whether these methods can be applied to construct a comprehensive educational supermodel with strengths across various capabilities, rather than being effective in only a singular aspect.
Jaromir Savelka, Arav Agarwal, Christopher Bogart
et al.
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three Python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a Python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.
Felix Dobslaw, Kristian Angelin, Lena-Maria Öberg
et al.
We see an explosive global labour demand in the Software Industry, and higher education institutions play a crucial role in supplying the industry with professionals with relevant education. Existing literature identifies a gap between what software engineering education teaches students and what the software industry demands. Using our open-sourced Job Market AnalyseR (JMAR) text-analysis tool, we compared keywords from higher education course syllabi and job posts to investigate the knowledge gap from a technology-focused departure point. We present a trend analysis of technology in job posts over the past six years in Sweden. We found that demand for cloud and automation technology such as Kubernetes and Docker is rising in job ads but not that much in higher education syllabi. The language used in higher education syllabi and job ads differs where the former emphasizes concepts and the latter technologies more heavily. We discuss possible remedies to bridge this mismatch to draw further conclusions in future work, including calibrating JMAR to other industry-relevant aspects, including soft skills, software concepts, or new demographics.
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning. Very recent advances in artificial intelligence have resulted in code generation models that can produce source code from natural language problem descriptions -- with impressive accuracy in many cases. The wide availability of these models and their ease of use has raised concerns about potential impacts on many aspects of society, including the future of computing education. In this paper, we discuss the challenges and opportunities such models present to computing educators, with a focus on introductory programming classrooms. We summarize the results of two recent articles, the first evaluating the performance of code generation models on typical introductory-level programming problems, and the second exploring the quality and novelty of learning resources generated by these models. We consider likely impacts of such models upon pedagogical practice in the context of the most recent advances at the time of writing.
This book presents detailed discussion on the role of higher education in terms of serving basic knowledge creation, teaching, and doing applied research for commercialization. The book presents an historical account on how this challenge was addressed earlier in education history, the cases of successful academic commercialization, the marriage between basic and applied science and how universities develop economies of the regions and countries. This book also discusses cultural and social challenges in research commercialization and pathways to break the status quo.