27300004 - Research in Smart Environments for Management: Interactions, Data and Services
| Niveau de diplôme | |
|---|---|
| Crédits ECTS | 6 |
| Volume horaire total | 4E+1 |
| Volume horaire CM | 40 |
Responsables
Objectifs
Smart environments have the potential to enable users to seamlessly engage and interact with their immediate surroundings. This has been made possible by the introduction of smart technologies, coupled with service-oriented solutions. Recent advances have opened a new era for the exploitation, processing, and analysis of data, facilitating the vision of intelligent environments. Nevertheless, several challenges and obstacles remain for its adoption in the field of management. This module provides students with foundational research training and a solid grounding in research methods and techniques, covering issues related to big data, as well as human-computer interaction and user experience.
Estimation of private study (outside of contact hours): 10 hours
DIMENSION OF SOCIAL RESPONSIBILITY
The course emphasizes ethical and responsible design and management of smart environments. Topics include data privacy, algorithmic bias, inclusive technology design, and sustainability in digital infrastructure.
TARGETED KNOWLEDGE AND SKILLS
By the end of this course, students will be able to:
Understand the fundamental components and structure of smart environments, including embedded sensors, data collection platforms, service-oriented architectures, and AI-driven decision-making systems.
Analyze the role of human–computer interaction and user experience in shaping intelligent systems and digital services, drawing on research in adaptive and personalized environments, such as work on emotion-aware learning systems and gamification strategies.
Develop a critical understanding of data-driven management challenges, including the collection, processing, and governance of large-scale sensor and behavioral data.
Critically assess the social and ethical implications of smart technologies, especially in relation to data privacy, and responsible service design.
Apply interdisciplinary research methods (qualitative, quantitative, and design-oriented) to investigate and assess smart environments in various management contexts.
Articulate how interaction traces can feed into adaptive and responsive smart environment designs.
Propose research questions and conceptual models that reflect the complexities of intelligent, data-rich, and interactive systems in organizational and service ecosystems.
By weaving in interaction/gamification insights, IoT/data infrastructures, and privacy-aware service architectures, the course is anchored in cutting-edge research while highlighting concrete applied methodologies relevant to management -in smart environments-.
Estimation of private study (outside of contact hours): 10 hours
DIMENSION OF SOCIAL RESPONSIBILITY
The course emphasizes ethical and responsible design and management of smart environments. Topics include data privacy, algorithmic bias, inclusive technology design, and sustainability in digital infrastructure.
TARGETED KNOWLEDGE AND SKILLS
By the end of this course, students will be able to:
Understand the fundamental components and structure of smart environments, including embedded sensors, data collection platforms, service-oriented architectures, and AI-driven decision-making systems.
Analyze the role of human–computer interaction and user experience in shaping intelligent systems and digital services, drawing on research in adaptive and personalized environments, such as work on emotion-aware learning systems and gamification strategies.
Develop a critical understanding of data-driven management challenges, including the collection, processing, and governance of large-scale sensor and behavioral data.
Critically assess the social and ethical implications of smart technologies, especially in relation to data privacy, and responsible service design.
Apply interdisciplinary research methods (qualitative, quantitative, and design-oriented) to investigate and assess smart environments in various management contexts.
Articulate how interaction traces can feed into adaptive and responsive smart environment designs.
Propose research questions and conceptual models that reflect the complexities of intelligent, data-rich, and interactive systems in organizational and service ecosystems.
By weaving in interaction/gamification insights, IoT/data infrastructures, and privacy-aware service architectures, the course is anchored in cutting-edge research while highlighting concrete applied methodologies relevant to management -in smart environments-.
Contenu
COURSE OUTLINE
Session 1: Introduction to Smart Environments & Digital Transformation in Management
Session 2: Human-Computer Interaction and User Experience in Services
Session 3: Data in Smart Environments: Big Data, Sensors, and AI
Session 4: Ethical and Social Challenges in Smart Environments
Session 5: Research Methods: Case-based, Qualitative, Quantitative, and Design-Oriented Approaches
Session 6: Case Study Analysis + Group Work
Session 7: Presentations and Wrap-Up
Session 1: Introduction to Smart Environments & Digital Transformation in Management
Session 2: Human-Computer Interaction and User Experience in Services
Session 3: Data in Smart Environments: Big Data, Sensors, and AI
Session 4: Ethical and Social Challenges in Smart Environments
Session 5: Research Methods: Case-based, Qualitative, Quantitative, and Design-Oriented Approaches
Session 6: Case Study Analysis + Group Work
Session 7: Presentations and Wrap-Up
Bibliographie
PRESCRIBED TEXTS AND PUBLICATIONS
Jararweh, A. A., Al‑Ayyoub, M., Al‑Ruithe, M., & Benkhelifa, E. (2020). Environmental monitoring framework using data fusion and software-defined systems. Future Generation Computer Systems (focus: smart city data infrastructure)
TEXTS AND PUBLICATIONS OF IAELYON FACULTY ON THE SUBJECT OF THE COURSE
1. Lavoué, E. et al. (2019). Adaptive Gamification for Learning Environments. IEEE Transactions on Learning Technologies (focus: personalization & engagement)
2. Senda Romdhani, Genoveva Vargas-Solar, Nadia Bennani & Chirine Ghedira (2021). « QoS-based Trust Evaluation for Data Services as a Black Box ». IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, septembre 2021, Chicago (États-Unis)
3. Firas Zouari, Chirine Ghedira, Nadia Kabachi & Khouloud Boukadi (2021). « Towards an adaptive curation services composition based on machine learning ». 2021 IEEE International Conference on Web Services (ICWS), 10 septembre 2021, Chicago (États-Unis), pp. 73-78.
4. Fuchs, B., Prié, Y., Mille, A., & Cordier, A. (2008). An approach to user-centric context-aware assistance based on interaction traces (MRC 2008)
Jararweh, A. A., Al‑Ayyoub, M., Al‑Ruithe, M., & Benkhelifa, E. (2020). Environmental monitoring framework using data fusion and software-defined systems. Future Generation Computer Systems (focus: smart city data infrastructure)
TEXTS AND PUBLICATIONS OF IAELYON FACULTY ON THE SUBJECT OF THE COURSE
1. Lavoué, E. et al. (2019). Adaptive Gamification for Learning Environments. IEEE Transactions on Learning Technologies (focus: personalization & engagement)
2. Senda Romdhani, Genoveva Vargas-Solar, Nadia Bennani & Chirine Ghedira (2021). « QoS-based Trust Evaluation for Data Services as a Black Box ». IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, septembre 2021, Chicago (États-Unis)
3. Firas Zouari, Chirine Ghedira, Nadia Kabachi & Khouloud Boukadi (2021). « Towards an adaptive curation services composition based on machine learning ». 2021 IEEE International Conference on Web Services (ICWS), 10 septembre 2021, Chicago (États-Unis), pp. 73-78.
4. Fuchs, B., Prié, Y., Mille, A., & Cordier, A. (2008). An approach to user-centric context-aware assistance based on interaction traces (MRC 2008)
Contrôles des connaissances
Individual grade
Research Report
Other grade(s)
Presentation
Weight: 40/60
Research Report
Other grade(s)
Presentation
Weight: 40/60
Informations complémentaires
TEACHING METHODS
Lectures with interactive Q&A;
Practical labs on data and interaction modeling
Group coaching on research projects
NATURE OF MATERIALS
Academic journal articles
White papers from industry (IBM, Accenture, McKinsey)
Research methodology guides
TEACHING INNOVATIONS AND USE OF TECHNOLOGY
Use of collaborative platforms
PRE-REQUISITES IN TERMS OF KNOWLEDGE AND SKILLS
Basic knowledge of management principles
Familiarity with digital technologies or willingness to explore them
Interest in research, data, and emerging technologies
ADVISED PRIOR READING
“Competing in the Age of AI” – Iansiti & Lakhani
“Data Science for Business” – Provost & Fawcett
Lectures with interactive Q&A;
Practical labs on data and interaction modeling
Group coaching on research projects
NATURE OF MATERIALS
Academic journal articles
White papers from industry (IBM, Accenture, McKinsey)
Research methodology guides
TEACHING INNOVATIONS AND USE OF TECHNOLOGY
Use of collaborative platforms
PRE-REQUISITES IN TERMS OF KNOWLEDGE AND SKILLS
Basic knowledge of management principles
Familiarity with digital technologies or willingness to explore them
Interest in research, data, and emerging technologies
ADVISED PRIOR READING
“Competing in the Age of AI” – Iansiti & Lakhani
“Data Science for Business” – Provost & Fawcett