This way, we narrow the scope of the present paper by focusing on two properties: students’ comfort and energy efficiency. We aim to integrate the ICTs to monitor and manage both of them; therefore, IoT devices are responsible for detecting comfort levels and energy efficiency on the campus and take consequent corrective action. We propose to conceptualize groups of smart devices that could be used to achieve a determined goal by acting as physical-world proxies for agents. For instance, an agent is responsible for improving energy efficiency and comfort in a given classroom, and it senses and actuates on the physical world (e.g., classrooms) through IoT sensors and actuators.
According to Eurostat and the European Commission report in Education and Training Monitor 2019, more than 31% of the European population is currently enrolled in educational programs. This percentage only includes physical-based learning. However, in recent years remote learning and distance education have grown significantly [3
]. Hence, more than 138 million European people spend a considerable amount of their time in educational facilities (schools, universities, colleges, etc.). Most of these facilities were constructed a long time ago to rapidly address the educational needs of growing local populations due to the societal changes in which young adults began to complete a full education plan: primary school, high school, and university/vocational training. At that time, educational institutions were large infrastructures to allocate all students, faculty members, and staff. However, little or no attention was paid to the overall comfort of these environments—understood as a measure that balances the wellbeing of all users, the efficiency of the processes involved, and the pro-environmental footprint of their facilities.
Recent studies have suggested that comfort in educational environments is a critical parameter for the success of learning and the evolution of society [4
]. Comfort is usually related to individual and isolated parameters such as air quality, temperature, or noise [5
]. Measuring these parameters can be tackled seamlessly with unobtrusive equipment as an enabler to obtaining reasonable—yet incomplete—partial conclusions [6
]. Indeed, much effort has been made to improve ICT-based solutions in the direction of more accurate and more complete systems (e.g., including more local variables) [7
]. However, these recurrent solutions typically fail at quantifying the side effects of measuring comfort involving external parameters to the educational environment that still have a great impact on its associated issues (e.g., overall sustainability, energy efficiency, learning and teaching performance, etc.). For instance, they are unable to address dilemmas such as whether it would be worth increasing the energy consumption to keep the optimal thermal conditions in order to ensure an improvement in the students’ academic output or not.
In essence, current ICT-based proposals to monitor comfort either do not deal collectively with the vast amount of internal and external parameters to measure them, or only provide local (i.e., partial) qualitative views of comfort as they are more focused on keeping the technological paradigm of cost-effectiveness [5
]. Hence, existing developments are incremental, concerning a conceptual and technological paradigm that remains unchanged. Understanding, monitoring, predicting, and optimizing comfort in educational environments requires a holistic and cross-layer view able to frame and quantify the dynamic and nonlinear relations of their involved users [8
]. Indeed, addressing the comfort in educational facilities cannot be tackled in a linear way since several interdependent parts are continuously changing. Therefore, it is safe to say that comfort in educational environments has remained under-sampled for years mostly due to the complexity of objectively quantifying and acting on it.
Specifically, authors have examined, measured, and analyzed all the potential external (e.g., available open data, weather information, architectural issues, etc.) and internal (e.g., thermal or acoustic data) variables affecting such comfort to (1) quantify, monitor, predict and optimize comfort in physical and, eventually, virtual educational environments; (2) enhance overall sustainability and (3) overcome potential issues in the teaching-learning process. The proposed structural model of our SC will help to predict the impact of the distinct institutional policies on comfort and, as such, it will encourage drivers to address changes such as conducting active learning methodologies, adopting eco-friendly initiatives to reduce environmental footprint toward carbon neutrality, or incorporating renewable energies to save natural resources.
Overall, our research proposes a radical paradigm shift and the use of IoT technology in monitoring and optimizing comfort in university learning environments, where the frame for analysis and modeling of the comfort parameter holistically covers the internal and external meta-dimensions, as a whole, that characterize the socio-environmental interactions of three strategic stakeholders: teaching and learning community, facility management staff, and energy providers.
If these dimensions, and their impact on comfort, were defined, quantified, and validated through innovative scientifically-grounded methods, this would drive the conception of a new technology able to transform the current generation of comfort analysis in physical and virtual educational environments. This achievement will endow them with a completely novel functionality to improve their sustainability while helping to understand, design, populate, monitor, and perceive comfortable learning environments.