No abstract

The Covid-19 pandemic has brought about new challenges to educational institutions around the world, as there has been a sudden interest in distancelearning solutions in the offering of disciplines, including the science and technology ones. Distance-learning will have a direct impact on the training of professionals that will develop the bespoke Internet of Things (IoT) solutions that homes, offices, companies, and cities are increasingly dependent on.Regardless, the training for IoT needs to consider the particularities of wireless communications, as it is essential to guarantee communication in any IoT solution. However, this training is hindered by the difficulty of conducting remote experiments that reproduce the wireless channel behavior. Besides, learning the main theoretical concepts of wireless connectivity would be facilitated by the adoption of practical methods centered on the student, the socalled inductive training methods. This paper presents a remote, low cost, open-source network emulation environment capable of reproducing the behavior of nonguided, low-power links under different configurations. The emulation environment incorporates inductive approaches in the experimentation with wireless connectivity while following a consolidated project methodology. This environment was offered at a postgraduate course in the first semester of 2020. An end of course survey with the students indicated that the environment contributed to the comprehension of the principles governing the over-the-air connectivity. The evidence suggests that this remote environment is a useful tool for academic investigations of the particularities of low-power wireless channels.

We are thankful to Companhia Paranaense de Energia (COPEL) for funding support in the current R&D project, under project ANEEL PD-02866-0508/2019.

The multidisciplinary aspect of Internet of Things (IoT) requires teaching approaches that can support this characteristic. Research has shown that inductive teaching methods can contribute to deeper learning. The development of the three‐phase methodology (TpM), an approach that guides the many disciplines involved in the creation of IoT solutions, precipitated the development of strategies for its implementation. To test these strategies, we propose a new learning environment that uses the TpM in a postgraduate course in IoT where students are introduced to the concepts required to design, build, and test IoT solutions. The adoption of the TpM combined with extensive experimentation can produce a learning environment that encourages students’ active participation in the activities and facilitate the retention of the concepts learned. To evaluate the effectiveness of the proposed approach, we compared the results of an earlier edition of the course (2019), when the approach was not used, to two more recent ones (2020 and 2021) when it was. The same syllabus and rubrics were used in all three editions of the course. The final group projects’ grades and satisfaction surveys were used to evaluate the students’ performance and motivation. Findings indicate that the 2020 and 2021 classes performed better than the 2019's, as their final group project grades were considerably higher, indicating that the approach has an impact on the development of students’ transversal skills. The proposed learning environment encourages active participation in the activities and facilitates the retention of the concepts learned.

Teaching has employed more and more digital and online tools, especially nowadays with the COVID-19 pandemic. IoT has been notoriously troublesome for teaching due to its multidisciplinary nature, involving fields such as computing, electrical engineering, business, humanities studies, and others. Several methodologies have been developed to teach IoT and create solutions, including the Three Phase Methodology (TpM). This paper presents the development of an Integrated Digital Learning Environment (IDLE-IoT), for distance teaching of IoT, based on TpM and Bloom's Taxonomy, integrated with a hardware platform.