As algorithmic systems increasingly mediate human activities across diverse domains, they shift more responsibility for data collection onto users, fundamentally altering the nature of data work. This paper examines the implications of this shift by investigating student-led data collection and automated feedback interpretation using a mobile, multimodal learning analytics (MMLA) tool designed to coach oral presentation skills. Our findings reveal that while this user-controlled data collection provides greater flexibility, it also imposes speculative labor, compelling students to adjust their behavior to align with perceived standards of good data even when such changes are unwarranted. The study highlights the often-overlooked informal labor involved in managing the socio-material conditions of data collection, emphasizing the need for MMLA tools that offer adaptive support and guidance. These insights extend to algorithmic system design in educational and professional contexts, advocating for systems that balance user autonomy with workload-minimizing guidance to achieve equitable accountability.

The abilities of Augmented Reality to overlay learning materials with contextual dynamic information and of software simulations to allow rapid and safe exploration of complex engineering concepts have already been established as beneficial in education. Access to both technologies in the classroom has been rather limited due to costs and hardware requirements. With the intention to bring these two technologies closer to engineering students, we created Virtual Circuits: a low-cost simulator app of electrical circuits designed for the classroom and autonomous study. Virtual Circuits requires only a smart-phone and a set of plastic tokens and, with the help of Augmented Reality and a simulation engine, it allows electrical engineering students to quickly explore complex concepts in their field. In this article we present the Virtual Circuits app and the results of our first field test in a real electrical engineering classroom at our university. The collected feedback from students was overwhelmingly positive hinting at tangible educational benefits and improvements in future versions of our app.

Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.

Nowadays, microphone arrays are used in many applications for sound-source localization or acoustic enhancement. The current Micro-Electro-Mechanical Systems (MEMS) technology allows the development of networks of microphone arrays at a relatively low cost. Unfortunately, the evaluation of these networks requires controlled acoustic environments, such as anechoic chambers, to avoid possible distortions and acoustic artifacts. In this paper, we present a partial reconfigurable FPGA platform to emulate a network of microphone arrays. Our platform provides a controlled simulated acoustic environment, able to evaluate the impact of different network configurations such as the number of microphones per array, the network's topology or the used detection method. Data fusion techniques, combining the data collected by each node, are used in this platform. In addition, our platform is also capable to converge to the ideal network with regards to power consumption, while still maintaining the desired level of sound-source localization accuracy. A graphical user interface provides a friendly control of the network and the parameters under test during the execution of the partial reconfiguration operations. Several experiments are presented to demonstrate some of the capabilities of our platform.

The current Micro-Electro-Mechanical System (MEMS) technology allows to deploy relatively low-cost Wireless Sensor Networks (WSN) composed of MEMS microphone arrays for accurate sound-source localization. However, the evaluation and the selection of the most accurate and power-efficient network's topology is not trivial when considering dynamic MEMS microphone arrays. Despite software simulators are usually considered, they are high-computational intensive tasks which require hours to days to be completed. Our demonstrator is an FPGA-based network emulator, which provides a fast network design-space exploration. The user can easily evaluate a network's topology with different nodes' configurations and multiple sound sources in matter of seconds. An intuitive graphical user interface hides from the user the dynamic partial reconfigurations needed by the network emulator to set the node's configurations. As a result, a probability map generated from the fusion of the output data from the nodes and an error on the estimation of the sound-source location are graphically represented.

The traditional recording of student interaction in classrooms has raised privacy concerns in both students and academics. However, the same students are happy to share their daily lives through social media. Perception of data ownership is the key factor in this paradox. This article proposes the design of a personal Multimodal Recording Device (MRD) that could capture the actions of its owner during lectures. The MRD would be able to capture close-range video, audio, writing, and other environmental signals. Differently from traditional centralized recording systems, students would have control over their own recorded data. They could decide to share their information in exchange of access to the recordings of the instructor, notes form their classmates, and analysis of, for example, their attention performance. By sharing their data, students participate in the co-creation of enhanced and synchronized course notes that will benefit all the participating students. This work presents details about how such a device could be build from available components. This work also discusses and evaluates the design of such device, including its foreseeable costs, scalability, flexibility, intrusiveness and recording quality.

In the Intelligent Distributed Environmental Assessment (IDEA) [3] system, sensors are used to measure noise and air pollution. The architecture is service-oriented, allowing application architects to combine services provided by the system platform in order to satisfy application needs. A typical user application may require a set of those services. Because of the sensor nodes' resource limitation, some services may not keep Quality of Service (QoS) levels stable over a long period, which could lead to Service Layer Agreement (SLA) violations. To maintain SLA, QoS levels must be estimated so that services can be properly invoked and combined to satisfy the user's QoS requirements. This paper proposes a novel approach to estimate QoS levels of web services. On the basis of monitored values of QoS parameters, a Naïve Bayes estimation model is developed to select the best-suited services. This paper also presents approaches adopted to monitor QoS levels of web services in the IDEA environment.