This article delves into the dynamic challenges confronting Higher Education Institutions (HEIs) globally, emphasizing the urgency for innovative solutions. The escalating number of students and the paradigm-shifting impact of the COVID-19 pandemic have forced HEIs to reevaluate pedagogical strategies, integrating blended and online learning models. Episodic change, characterized by distinct periods of transformation, necessitates a nuanced approach, emphasizing the need for flexibility, adaptability, and strategic planning. The article also highlights the imperative to update the skills of educators and students, with a focus on overcoming barriers to effective digital education implementation. Teaching and Learning Centers (TLCs) emerge as potential models for sustainable transformation, yet challenges persist in defining their competencies and roles. The paper advocates for a holistic approach, prioritizing strategic planning, faculty development, and clear frameworks for units like TLCs. The article concludes by emphasizing the innovative potential of TLCs and proposing future directions, including the exploration of competencies required for TLC development and alternative models for navigating episodic changes beyond the context of the pandemic, and strategies for addressing challenges posed by Artificial Intelligence (AI) integration. The multifaceted role of AI in personalized learning, analytics, and administrative processes presents opportunities and challenges, including ethical considerations, data privacy, and inclusivity.
Accurate sales forecasts help companies make business decisions. However, quantitative forecasting is difficult due to complex environmental changes and company characteristics. In this study, we attempt to analyze cyclical data for forecasting.
This paper focuses on learning outcomes in the context of Computer Science Higher Education with the integration of Artificial Intelligence (AI) tools. As the rapid development of technological tools reshapes educational landscapes, educators face the challenge of effectively incorporating AI tools into pedagogical practices. While AI tools in Computer Science promise advantages, such as faster code generation and the optimization of learning time, concerns persist about potential over-reliance leading to reduced student learning. This study focuses on gathering evidence from students’ perspectives on the benefits of AI tool utilization and aims to scrutinize teachers’ practices in overcoming challenges associated with verifying learning outcomes. A survey questionnaire designed for students and teachers explores the areas of AI tool application, perceived benefits, impact on learning, practical application, and methods for validating student learning in the presence of AI. This study contributes to the ongoing discourse on the intersection of AI, pedagogy, and student success in Computer Science Higher Education.
The problem of understanding trust relations in modern society is extremely interesting nowadays. In our project, we are going to use Multi-agent systems (MAS) to model and reproduce experiments from social sciences to better understand how the relation of trust is shaped in society. In order to perform such experiments it is necessary to accurately represent trust and trustworthiness in MAS. In this paper, we introduce the model and an experimental implementation of benevolence, one of the most important components of trustworthiness.
More than 150 years have passed since the start of the gas business in Japan, and with the advancement of technology, gas has now become indispensable for making our lives comfortable and convenient. Furthermore, in recent years, fuel cells and high-efficiency water heaters have become more widespread, contributing to energy conservation and CO2 reduction in our daily lives. On the other hand, it is self-evident that the more gas is used, the more various opinions from users will increase. In recent years, there has been a movement for individuals to express their opinions on social media and other online platforms in an attempt to solve problems. The purpose of this study is to develop a new tool to visualize online opinions about gas, which has become increasingly used, and to provide information that will help gas utilities in their improvement activities quickly and simply.
This paper addresses the global challenge of food production losses caused by plant diseases, pests, and nitrogen stress, focusing on the specific context of Ethiopia where cereal crop yields face a significant annual decline of 20–30
In an era where artificial intelligence (AI) is reshaping various facets of life, its integration into academic learning has become a focal point of educational innovation. The study explores the impact of AI-powered platforms on academic learning, particularly focusing on their purpose and the methods used for evaluating learning processes. Drawing upon a comprehensive review of recent literature and empirical data from a survey of 610 individuals, this research provides insights into how AI technologies are being adopted in educational settings and their effects on learning outcomes. The study synthesizes findings from various scholarly articles, highlighting the adoption and effectiveness of AI in enhancing administrative functions, teaching quality, and personalized learning experiences. It also delves into the challenges and opportunities presented by AI in education, including ethical considerations and the balance between learner-centered and platform-centered approaches. Empirical data from the survey further enriches the study, offering a real-world perspective on the reception and perceived impact of AI-powered educational tools. This data provides a unique lens through which the practical implications of AI in education are examined, considering factors such as learner engagement, satisfaction, and academic performance. In conclusion, this research not only underscores the transformative potential of AI in academic learning but also addresses the complexities and nuances involved in its integration. It aims to contribute to the ongoing discourse on AI in education, providing valuable insights for educators, policymakers, and technology developers.
The term Ambient Intelligence (AmI) has been a topic of interest for many years, as it has the potential to transform services and environments—from living and working spaces to hospitals. Context awareness is a prerequisite for the development of truly intelligent services, i.e. it is necessary to detect and understand the state of the space as well as events/activities taking place. Additionally, for the complete adaptation of the space to the user, it is necessary to take the user’s preferences into account. Defining user preferences is a very complex area with numerous approaches, starting with the development of simple automated services in a smart home, where the user is offered control of a simple set of rules to define their wants and needs. The paper conducts an analysis of preference definitions in different environments and proposes a preference definition for the use case of smart lighting and smart heating and cooling system in a Multi-Agent System approach. This proposed definition forms the basis for effective negotiation between users with different preferences, where conflicts are inevitable.
Collecting personal user data helps software developers to improve the product. However, it must be conducted in an consensual and transparent manner. In this paper, we compared several approaches to collecting personal data from users in the context of an informal e-learning system. More specifically, we asked the learners to donate their personal data by a filling an optional questionnaire and explicitly stated how the data will be used by the developer. We designed and implemented an open-source package for gathering the data, and highlighted some of the ethical and technical aspects. We then experimented with several alternative settings for the questionnaire to optimize the consent rate. We conclude that our approach to data collection can be an effective supplement to existing data collection practices.
This paper addresses the hazards and benefits that large language models (LLMs), such as ChatGPT, bring to the instruction of Academic Writing for Computer Science students. On the one hand, advancements in artificial intelligence (AI) may decrease students’ motivation to study academic writing and tempt them to generate texts using chatbots. This reluctance to invest effort in practicing academic writing can question the learning outcomes in writing courses. On the other hand, AI can aid students in achieving greater autonomy by enabling them to check their writing independently. What is more, ChatGPT can reduce instructors’ workload by helping them produce teaching materials and answer the students’ simple questions that do not require expertise. We, therefore, conclude that LLMs are inevitable, and writing instructors should teach students to use AI responsibly to benefit from its powers while avoiding unadvisable consequences of the reckless use of AI.
This research paper proposes the implementation of a teaching strategy for the Lean software development course targeting third-year undergraduate students. Additionally, the intermediate results of applying the proposed methodology are provided. The primary objective is to suggest an instructional approach and enhance the overall learning experience. The proposed methodology employs a diverse set of research methods, including both qualitative and quantitative approaches, to comprehensively assess the outcomes associated with the Lean software development course. Specifically, the teaching strategy encompasses preliminary grade selection, mid-oral exams, quizzes, project-based learning, and a final exam. The proposed strategy will inform future advancements in curriculum design and instructional strategies, ultimately benefiting both students and the broader software engineering community.
In the dynamic landscape of contemporary corporate operations, extracting knowledge from business processes has emerged as a pivotal factor influencing the success and sustainability of companies. This paper delves into the growing significance of using knowledge extracted from business processes to achieve organizational goals and objectives, shedding light on how it has become central to a company’s overall functioning. As businesses increasingly rely on streamlined processes to gain a competitive edge, the impact of insufficient data quantity on these processes must be balanced. Many companies need help with the challenges posed by inadequate data, impeding their ability to make informed decisions and hindering operational efficiency. This research explores the intricate relationship between process mining and data quantity, unraveling the repercussions of suboptimal data practices on organizational performance in business intelligence. It aims to provide a novel approach involving data augmentation to mitigate this problem, allowing companies that rely on poorly logged processes to benefit from process mining and business process alignment.
This research focuses on the transformative potential of integrating electric vehicle (EV) technology and artificial intelligence (AI) to revolutionize education by allowing students to apply their knowledge in real-life applications and observing their performance. This study employs a comprehensive approach, integrating qualitative and quantitative surveys to assess the impact thoroughly of EV technology and AI on performance of students and their learning abilities. By examining how EV technology and AI can be incorporated into educational practices, the research aims to redefine traditional learning models and prepare students for the evolving demands of the future. The proposed work-in-progress framework allows for an ongoing exploration of strategies, challenges, and opportunities in leveraging EV technology and AI to enhance educational experiences. This investigation contributes to the development of a forward-thinking educational ecosystem aimed at empowering learners with the competencies required for success in a technologically advanced society.
In recent years, social media has developed significantly, and many studies have been conducted on the effectiveness of advertising using social media. It has also been reported that in Japan, the types of media used differ depending on the age group. In this study, we used consumers belonging to several types of household structures as agents, and constructed an agent-based model that has four stages of consumer consciousness. Using the constructed model simulator, we evaluated social media and television advertising on a household basis. As a result, by changing the conditions, we were able to obtain results that show that differences in household composition lead to changes in advertising effectiveness. In addition, we were able to confirm that advertising effectiveness changes by changing the presence or absence of network effects in information exchange within households and the advertising effectiveness of social media and television.
The focus of this work is to propose a new model of medical care based on an optimal and broad vision for the ideal management of an emergency room from APH care (pre-hospital care) until reaching the classification of the patient based on the TRIGE system in such a way that in an emergency room through logical technological tools such as Bigdata (BD) and Machine Learning (ML), Deep Learning (DL), as well as the implementation of IoT, as there is no previous research it avoids saturation of the rooms, provide methods to save as many lives as possible and no preliminary results can be reached because of the small amount of medical data, data sources, and the difficulty of the different laws that protects personal and medical data.
This paper presents a study on dynamic vanpooling systems using multi-agent modeling. Our focus is on optimizing vanpooling services for passengers with shared destinations, particularly those commuting to work or educational institutions. To tackle the complexity of the assignment problem, we introduce an Agglomerative Hierarchical Clustering-based method for selecting optimal pickup points, minimizing passenger walking distances. Additionally, we propose a branch-and-bound algorithm for efficient van allocation. To evaluate the impact of vanpooling on traffic, we conduct simulations with varying van capacities and compare them to scenarios without ridesharing and with smaller van capacities. Real-world data from the Lyon network are used for experimentation. The results demonstrate that increasing van capacity significantly reduces total travel time, distance, and the number of trips and vehicles required. These findings underscore the potential of dynamic vanpooling to mitigate congestion and enhance travel efficiency in urban areas.
This article presents a research project that aims to assist individuals with cerebral palsy to improve their communication abilities and achieve greater autonomy using electromyography (EMG) technology. EMG records the electrical activity of muscles, providing insights into muscle physiology and activation. The proposed system would filter EMG signals, adapt them to the user's needs, store data, and present it through a graphical interface. The system targets those with cerebral palsy, facing challenges performing body movements and communication. The article provides background on EMG and its applications, especially for those with disabilities. It outlines common motor impairments in cerebral palsy. The methodology involves classified EMG signals over time. Results demonstrate the superior performance of random forests over single decision trees for prediction. Overall, the project leverages EMG technology to enhance communication and autonomy for cerebral palsy patients.
The European Climate Law aims to combat energy-inefficient buildings by prohibiting the rental of the most energy-consuming housing units. These restrictions, designed to address greenhouse gas emissions and energy conservation, are at the forefront of metropolitan concerns as part of their efforts toward ecological transition. MUST-B (integrated land Use Model—Transport application to the Urban area) is a Land Use Transport Interaction (LUTI) model that simulates households and workplaces location choices. It models and simulates the interaction of both the population of an urban area through their residential choices with activities and jobs, as well as the various transportation modes to meet daily mobility needs. This agent-based tool allows for understanding the complexity of an urban area based on individual behaviors giving rise to collective phenomena. In this article, our focus is on the “land use” component of the tool, specifically emphasizing the “multi-agent” aspect by developing two populations of agents in close interaction during the simulation: households and their residences. We also explore an example application related to a European regulation for the withdrawal of high-energy-consumption housing.
Agile methodologies are increasingly dominant in software development, focusing on collaboration, adaptability, and predictable progress by continuous iterations. Within Agile, the Product Owner (PO) plays a vital role in shaping the product vision, understanding stakeholder priorities, and continuous conversation with the development team. We deal with the problem of educating, or training, a Product Owner. Agile serious games offer effective training for novice POs through experiential learning. This paper aims to gather evidence on the PO’s role, selecting and comparing popular serious games based on factors like time requirements, resource needs, incorporation of Agile rituals, targeted methodologies, and recommended participants. Data collection involves existing documentation, published materials, and firsthand accounts. Qualitative analysis reveals distinct game characteristics and suitability for Agile training.
In recent years, the market has been experiencing great shocks like coronavirus pandemic. Crisis times significantly change market conditions, and companies have to adapt very quickly in order to keep up with new features and survive in the market. A large number of companies go bankrupt in times of crisis; the main reason is that the way companies are managed does not allow them to quickly change the direction of development, listen to other people’s opinions, or develop new business strategies, perhaps they did not have enough flexibility. Many different studies describe how agile technologies are applied at different levels, such as effective HR, or only in certain types of companies, such as healthcare, and there are almost no such that describe a completely new approach to management that allows flexibility at all levels, as much as possible. This study attempted to find a way to manage that would suit a large number of companies that combines all the best practices for using agile methods, as well as to determine how satisfied employees are with the chosen methods of various companies. An unusual management method was considered—holacracy, it was determined how this approach differs from the others, what are the advantages and disadvantages, whether the strengths of holacracy allow you to build an effective business. Data were collected from various companies that use different management methods in order to determine on their basis how satisfied the employees are with the company, how easy it is for the company to change the development vector. Based on the collected data and the conducted interviews, recommendations were made that could make the company’s work more efficient and their employees more satisfied with their work.
