Despite comprehensive Bodies of Knowledge (BoKs) documenting core knowledge across software engineering, computer science, information systems, and emerging computing fields, a critical gap persists: methodologies for integrating this knowledge into coherent competency-based curricula that prepare graduates for professional careers remain underdeveloped. This paper presents a competency-mapping methodology that bridges Bodies of Knowledge and competency frameworks to design computing curricula. We demonstrate this methodology through ISANUM, a five-year engineering degree program featuring 23 competencies organized into five thematic blocks, each with explicit mappings to 494 knowledge topics from 34 Computing Knowledge areas defined in Computing Curricula 2020. The program integrates three specialized pathways (Software Engineering, Data Engineering & Data Science, and Information Technology) with mandatory work-study programs, ensuring graduates develop both theoretical foundations and practical workplace competencies. Our contribution provides computing educators with a replicable methodology for translating Bodies of Knowledge into assessable competency frameworks, supported by a semantic wiki infrastructure (ISANUMpedia) enabling collaborative curriculum understanding, maintenance and evolution.

Adaptive feedback strategies have shown their effectiveness in domains such as health, education, and residential energy, yet software energy feedback remains largely static and primarily oriented toward experts rather than end users. As a consequence, the long-term behavioral impact of adaptive feedback in software energy contexts is still poorly understood. This paper evaluates whether adaptive feedback strategies can sustain user compliance and energy savings in software usage under realistic behavioral dynamics. Using a simulation-based experimental methodology grounded in empirical effects such as fatigue, novelty decay, and preference drift, we compare adaptive and non-adaptive software energy feedback strategies across multiple scenarios. The results show that adaptive software energy feedback consistently outperforms static approaches, particularly under non-stationary user behavior, and reaches effective performance within a few weeks under cold-start constraints. These findings highlight adaptivity as a key missing component of software energy feedback.

With growing concerns about computing's environmental impact, sustainable IT practices must engage all stakeholders-including end-users. This study investigates how user behavior influences software energy consumption through a mixed-methods approach: a campus survey (N=280) revealed that while 91.5% understand device energy use, only 55.3% recognize software's role, and just 46.8% modify habits despite 79.3% acknowledging individual responsibility. Follow-up interviews highlighted demographic divides, with older (25-64) and advanced-degree users showing greater awareness. A week-long field experiment at an IT company demonstrates that real-time feedback-especially disaggregated data showing top energy-consuming apps-shifted perceptions, reducing hardware's perceived influence by 29%. Though users adopted energy-saving actions (e.g., closing idle apps, switching to lightweight software), workplace priorities often limited sustained changes. Our findings prove user-centric interventions can raise awareness, but highlight the need for context-sensitive designs that balance energy efficiency with workflow demands. Thiswork establishes actionable pathways to reduce ICT's carbon footprint through behavior-aware software ecosystems.

In the context of the rising energy consumption of ICT and its environmental impacts, there is a need to raise user awareness on the energy impact of their ICT devices and software. We mapped the existing literature around this topic by conducting a systematic mapping study on software approaches for providing energy feedback. We have found that while providing energy feedback is common in the building sector under the guise of smart-home and smart grid applications, the ICT sector is sparsely monitored. When ICT is the patient of sustainability (Green IT/Green in IT) rather than the engine (IT for Green/Green by IT), we found that energy monitoring software are predominantly aimed towards experts (developers and researchers) and decision makers rather than end users. By contrast, when ICT is the driving force for sustainability, energy monitoring software are predominantly aimed towards end users. We believe this contrast in targeted actors shows the need for more publications in Green IT (Green in IT) for raising awareness and reducing software energy consumption in end users through the use of persuasive technology and energy feedback systems already in-use and tested in the building sector.

The digital transformation of our society has deeply impacted the way we interact, communicate, and learn. This new digital era raises exceptional challenges for teaching and learning, particularly in adapting to individual learning paces, providing adequate progress traceability, and ensuring inclusive solutions for diverse learners. This paper introduces an Autonomic Cyber-Physical System for Education (A-CPS-E) that addresses these challenges through a framework combining teaching and learning design patterns with autonomous system capabilities. The A-CPS-E implements the autonomic computing paradigm to adapt learning experiences across four instructional interaction modes and generates Augmented Interactive Learning Objects (AILO) using AI support to enhance the teaching and learning process. We deployed the system within the Erasmus+ Connect Unita project, engaging 484 learners and 100 teachers across 12 universities in Europe. Resulting in the creation of 14 international training programs, including 65 learning paths and 400 learning objects, with $91.3 \%$ remote engagement. Preliminary results have allowed us to evaluate the benefits of the A-CPS-E in providing adaptive, traceable, and inclusive educational experiences that transcend traditional boundaries in higher education.

This paper presents an integrated approach, of a multi-criteria decision-making framework and fuzzy multi-objective programming to optimize dispatching and rebalancing for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby, a fleet of self-driving electric vehicles are coordinated to service on-demand travel requests and eventually allowing multiple passengers to share rides. Specifically, the fuzzy analytical hierarchy process and the Fuzzy technique for order of preference by similarity to ideal solution are first integrated in order to analyze customer preferences, prioritize their attitudes toward autonomous vehicles, and then to rank fleet vehicles according to these prioritizations. Next, leveraging the ranks of vehicles, we introduce a new Multi-Objective Possibilistic Linear Programming (MOPLP) model, considering realistic constraints and handling the uncertain nature of some critical data affecting RAMoD systems. Three conflicting goals are considered simultaneously which are (i) to minimize the lost customer requests, (ii) to minimize the total transportation cost and (iii) to improve customer satisfaction. This MOPLP model is converted to an equivalent crisp MOLP through applying appropriate strategies and the goal programming method is called to solve this MOLP and find an efficient compromise solution. Finally, the applicability and efficiency of the proposed approach are presented through an illustrative example. Collectively, this work provides a unified framework for controlling and analyzing RAMoD systems, which includes a wide range of modeling options (e.g., the inclusion of the uncertain future demand), and provides the first correlation between the dispatching and rebalancing decisions, and the process of analyzing customer preferences toward autonomous vehicles.

In the past few years, education has undergone a major evolution with the introduction of digitalization. The emergence of asynchronous learning allows learners to engage with learning materials at their own rhythm (anytime and anywhere). In addition, personalized learning has been shown to improve learner engagement and achievement. It also helps educators and learners to have insights of the progress and difficulties encountered by each learner. However, the lack of a suitable feedback system limits its implementation. In order to complete this transition from face-to-face education to hybrid and remote learning, improve the face-to-face learner's experience, and have an adequate feedback system for personalized learning, Virtual Learning Environments (VLEs) are needed. This paper presents a proposal of VLE as a Service (VLEaaS) as a cloud type of cloud computing as-a-service used to provide learners with an individual, course, workgroup, and on-demand factory workspaces. We detail the impact of these environments on learners and teachers in the context of higher education. We also implemented and experimented a set of VLEs for computer science learners. Finally, we discuss and analyse the results based on the data collected by learning traces sensors to monitor the learning progress and process of each learner. In addition, we gathered user feedback that shows an effectiveness of such approaches and a high level of satisfaction among learners and educators.

The adoption of hybrid and distance learning modes, supported by pedagogical and digital innovations, has favored the availability of large volumes of learning traces, resulting from learners' interactions with digital learning environments and services. These traces represent the digital footprints in the form of actions such as logging on, navigating, passing, scoring, etc., throughout their learning process. With the practice of Learning Analytics (LA), these traces are exploited using several different approaches, to foster learners' success. However, educational organizations very often engage in the massive collection of both relevant and irrelevant traces, requiring significant resources (time, material, etc.) and making the preparation and analysis phases linked to these data complex. In this paper, we propose the adoption of a learning object-oriented instructional design methodology with a structured approach to learning outcomes and learning paths, enabling better LA practice for monitoring learners' progression, participation, and performance. This approach allows for the collection of relevant traces, optimal preparation, and effective analysis of learning traces, to take better advantage of the benefits of LA to improve and adapt learning processes. This also makes it possible to better measure the impact of instructional design in LA practice and vice versa.

The evolution of education ecosystems has histor-ically been linked to social, economic, and technological ad-vancements' culminating in the emergence of education of the future, what we might call Education 4.0. To comprehend the challenges and opportunities presented by this new educational era, it is imperative to examine the preceding educational revolutions, ranging from antiquity to the present day. Education 4.0 should respond to the learning challenges posed by mod-ern society's dynamic education needs, while drawing insights from the benefits of past education generations. In this new context, learners should become architects of their education, and institutions must adapt to cater to individual needs. To fully exploit the potential of technology in education, we require a conceptual model that allows us to optimize the benefits from its utilization while emphasizing new pedagogical approaches. This model must include all stakeholders in a common generic semantic space, enabling students to acquire competencies and teachers to design learning resources that precisely meet the needs of our society. Having this generic common semantic space would facilitate the integration of initiatives proposed by major players, such as the curricula recommendations proposed by the ACM and IEEE. The research results are being applied in the development of the ISA NUM program, a new French computer science engineering training initiative. The program leverages an ontology-driven competency model, aligned with the ACM Computing Curricula 2020, to address the current societal demands in the field of computer science. This model forms the basis for an intelligent cyber-physical education system that integrates virtual and physical learning environments, integrating advanced technologies like virtualization, artificial intelligence, IoT, and semantic web. Moreover, this system integrates learning analytics features aimed at facilitating designing and developing personalized, flexible, and dynamic learning paths. Education 4.0 represents a promising future for education, aligning it with the ever-evolving digital landscape and the specific needs of modern learners and society.

With the rise in greenhouse gas emissions (GHGE), the need for sustainable solutions amid the environmental crisis is growing. Information and communication technologies (ICT) reduce emissions in other sectors, but they also have a rising carbon footprint and increased energy consumption. Our societies today rely on these technologies, and human needs and interactions are the main driving force behind their widespread use. Therefore, it is crucial to address the human role in energy consumption and carbon emission of ICT. Our work aims at raising end user awareness about ICT, and in particular about software, with an objective of reducing software energy through user behavior change. Motivated by a lack of user-centric approaches in energy feedback systems, we propose rigorous field experiments and surveys using persuasive technologies to optimize software energy consumption in ICT. We propose to first categorize feedback characteristics and audiences through a taxonomy. Then, we aim to study feedback impact on user awareness and software energy reduction through quantitative and qualitative field studies.

Nowadays, we observe an increasingly rapid growth of connected objects and data produced at all levels of modern society and particularly in the industrial world. A new industrial revolution has therefore appeared, under the name of Industry 4.0, aiming in particular at designing and implementing so-called cyber-physical systems allowing to face new important challenges linked to an intelligent use of ICT technologies in order to be able to propose products and services adapted to the society of tomorrow. This chapter introduces several referential architectures for Industry 4.0 and proposes a 5C layered architecture enhancement intended to facilitate designing, developing and managing Cyber Physical Systems. The two lowest layers are intended to cope with the integrability (connectivity) and interoperability (communication) challenges of the heterogeneous actors involved in CPS (people, things, data, services, etc.). The three highest layers are intended to incrementally integrate monitoring, analysis, planning and management capabilities required to allow coordination of CPS as well as cooperation and collaboration of Cyber-Physical Systems of Systems (CPSoS).

Monitoring the power consumption of smart and connected devices is a challenging task with heterogeneous devices and a variety of hardware and software configurations. In order to accurately monitor these devices and follow the speedy changes in their configuration, a new approach is needed. In this paper, we present an automated architecture and approach to empirically generate power models for a large set of devices. Our approach allows conducting automated benchmarks to collect power data and metrics, generating or updating accurate power models, and allowing software tools to query and retrieve the most accurate and up-to-date power model of a specific device configuration. We also present a proof-of-concept implementation for modeling the power consumption of Raspberry Pi devices. Finally, we conduct a comprehensive experiment, modeling the entire current lineup of Raspberry Pi devices with error margins as low as 0.3%, and then we discuss the impact of multiple device configurations on power consumption.

From antiquity until nowadays, educational systems have evolved in parallel with major social and economic changes, integrating pedagogical and technological innovations into the educational ecosystem and leading to the advent of a new educational revolution, called Education 4.0. The Education 4.0 paradigm allows both, to support educational organizations in the adoption of pedagogical and digital transformations, and at the same time to meet the needs of new generations of learners and teachers. The increasing use of ICT and the power of the Internet have fostered the emergence of a new generation of learners for whom technology is an integral part of their lives and therefore of their learning style. To respond effectively to the specific needs of these new learner profiles, educational organizations must adopt significant and ongoing transformations in the way we teach and learn. Enabling student-centered teaching, personalized learning paths, and access to a variety of heterogeneous educational resources have made educational processes too complex for the educator to effectively monitor the individual progress of each student. It becomes necessary to automate the management of learning processes to provide better answers to the evolving and specific needs of the different actors (students, educators, training program managers, companies, etc.). This research project aims at proposing an autonomous architecture of Cyber-Physical systems for Education 4.0. This architecture responds to the needs of educational systems, integrating digital technologies for the development of heterogeneous learning environments, capable of meeting the needs of a plurality of learners with different profiles and supporting teachers and training managers in the management of learning processes. This architecture offers the possibility of autonomous management of learning processes to analyze the progress of students and to prescribe the necessary recommendations to increase the chances of success, facilitating the mission of teachers.

In recent years, the number of connected devices and systems has increased exponentially, along with their daily use. Cyber-Physical Systems (CPS) are complex multi-layered feedback systems that combine computing resources and interaction with the physical environment using sensors and actuators. Energy is considered one of the major concerns driven by the increasing number of connected systems. In this paper, we review current research approaches in energy-aware CPS. We propose a novel architectural methodology to analyse and compare state-of-the-art approaches, on their architectural design and energy-related factors. We finally draw recommendations from our review on how to build energy-aware CPS solutions.

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

In online learning environments, the teacher provides students with a learning path to follow in order to acquire the expected competencies and skills.However, students' profiles are different as they can learn according to different learning paces or media content.Therefore, the actual learning path followed by each learner may vary from the initial path provided in the learning management system (LMS).This paper proposes an analysis of the learning paths followed by the students in order to identify and promote the most adapted learning processes in order to improve competencies and skills acquisition. PURPOSE OR GOALThe learning traces left by students in their learning environment could be exploited in order to better understand and guide learning processes.Unfortunately, with large-scale education, the analysis of different learning paths can be a complex task to be manually carried out by teachers.For this reason, our objective is to propose an approach to model, visualize, analyze and recommend the most efficient learning process in order to improve students' education experience and results. APPROACH OR METHODOLOGY/METHODSThe approach adopted is based on the learning traces left by the students following their interactions with the Learning Management System (LMS).After collecting, processing, and storing these learning traces, Process Mining technologies are used to analyze the data through an exploration of the learning process, as well as the students' learning paths. ACTUAL OR ANTICIPATED OUTCOMESThe first results obtained have made it possible to visualize the learning process, as well as the learning paths followed by each learner.They also provide analysis indicators for understanding and optimizing the learning process and the students' paths in digital learning environments.These results allow the stakeholders (training managers, teachers, and students) to improve the way they teach and learn. CONCLUSIONS/RECOMMENDATIONS/SUMMARYThis approach made it possible to comprehensively understand the learning processes and the learning paths of each learner, to visualize their differences, as well as their advantages and disadvantages.The analysis of the learning processes promoted a correlation study between the behavior of the learner (i.e. the number of connections between the sections of a course followed) during the learning process and their mark obtained on the final exam.The correlation coefficient of the evaluated courses was of the order of 0.49 and 0.53 respectively.Moreover, in order to improve the predictive model, it's necessary to implement advanced analysis: diagnosis, predictive and prescriptive based on the descriptive elements (Process visualization).This allows teachers to have an integrated tool for analyzing learning traces through a monitoring, diagnostic, alert, and early intervention system in order to better promote the success of students.