Far beyond very fine-grained, accurate data, movement-based interaction design often benefits from working with higher-level features to create engaging interactions. Yet, movement-based frameworks most often focus on the detection strategy without providing clarity on how to use such data for the design of user interactions. This purely technical approach makes it challenging for practitioners to explore feature extraction technology as a design material. This paper presents an approach that considers designers’ perspectives and attitudes about using movement-based features to propose a set of designer-interpretable descriptors and enhance their ability to use human motion in interaction design. In addition, a computational prototype is utilized to visually present the features and help designers better understand movement content. The investigation into the effects of designer-interpretable features on the concept ideation and design of motion-based interactions followed a user-centered approach, and its validity was established through a qualitative study conducted with interaction designers. Semi-structured interviews following a creative practice exercise were employed to evaluate how the computational prototype influenced interaction designers’ processes, allowing them to reflect on their experience and compare it to previous approaches when designing movement-based interactions. The study determined that interaction designers were able to leverage features to identify detection capabilities and enrich the ideation process. Moreover, feature visualization provided further insights into the characteristics of movement, which helped practitioners understand the interaction opportunities that come with it. • Designer-interpretable features enable non-technical practitioners to use motion analysis. • Spatial visualizations make group detection accessible without algorithmic expertise. • Five themes reveal how accessible features transform installation design practice. • Designers prioritize spatial patterns over biomechanical precision for public spaces. • Evidence-based iteration replaces technology-constrained design in motion systems.

Movement-based full-body interactions are increasingly being used in the design of interactive spaces, computer-mediated environments, and virtual user experiences due to the development and availability of diverse sensing technologies. In this context, the role of interaction designers is to find systematic and predictable relationships between bodily actions and the corresponding responses from technology. Sensor-based interaction design relies on sensor data analysis and higher-level feature extraction to improve detection capabilities. However, understanding human movement to inform the design of motion-based interactions is not straightforward if the detection capabilities of interaction technologies are unknown. We aim at understanding the problems and opportunities that practitioners—regardless of their technical background—perceive in using different motion-based full-body features. To achieve this, we conducted four separate focus groups with experienced practitioners, with and without technical backgrounds. We used a framework for the analysis of focus group data in information systems research to identify content areas and draw conclusions. Our findings suggest that most interaction designers, regardless of their technical background, consider motion-based feature extraction to be challenging and time-consuming. However, participants acknowledge they might use designer-interpretable features as a potential tool to foster user behavior exploration. Understanding how practitioners link sensor-based interaction design with feature extraction technology is relevant to design computational tools and reduce the technical effort required from designers to characterize the user’s movement.

This paper reflects on the effectiveness of current Learning Management Systems and proposes one whose information structure and interface are designed according to a specific educational model -one centered on learning activities. Likewise, the most significant results of evaluating the first version of the platform are presented.

Online discussion forums are a key element in virtual learning environments. The way learners participate in discussion boards can be a very useful source of indicators for teachers to facilitate their tasks. The use of a two-stage analysis strategy based on an agglomerative hierarchical clustering algorithm is proposed in this paper to identify different participation profiles adopted by learners in online discussion forums. Different parameters are used to characterize learners' activity (amount of posts, rhythm, depth of threads, crossed replies, etc). Participation profiles are identified and analyzed in terms of behavior and performance.

Due to the idiosyncrasy of online education, students may become disoriented, frustrated or confused if they do not receive the support, feedback or guidance needed to be successful. To avoid this, the role of teachers is essential. In this regard, instructors should be facilitators who guide students throughout the teaching-learning process and arrange meaningful learner-centered experiences. However, unlike face-to-face classes, teachers have difficulty in monitoring their learners in an online environment, since a lot of learning management systems provide faculty with student tracking data in a poor tabular format that is difficult to understand. In order to overcome this drawback, this paper presents a novel graphical educational monitoring tool based on faceted browsing that helps instructors to gain an insight into their classrooms' performance. Moreover, this tool depicts information of each individual student by using a data portrait. Thanks to this monitoring tool, teachers can, on the one hand, track their students during the teaching-learning process and, on the other, detect potential problems in time.

This paper describes our participation in the INTERSPEECH\n2009 Emotion Challenge [1]. Starting from our previous\nexperience in the use of automatic classification for the\nvalidation of an expressive corpus, we have tackled the\ndifficult task of emotion recognition from speech with real-life\ndata. Our main contribution to this work is related to the\nClassifier Sub-Challenge, for which we tested several\nclassification strategies. On the whole, the results were slightly\nworse than or similar to the baseline, but we found some\nconfigurations that could be considered in future\nimplementations.

In this paper, a generic pitch marks filtering algorithm (PMFA) is introduced in order to achieve reliable and smooth pitch marks\nfrom any input pitch tracking or marking algorithm. The proposed PMFA is a simple yet effective filtering process based on restricted dynamic programming, but very helpful for minimizing human intervention when creating large speech corpora. Moreover, this work introduces a novel pitch marking evaluation measure for directly comparing pitch marking algorithms with different location criteria. The experiments demonstrate that the proposed PFMA improves the results of the input state-of-the-art pitch tracking and marking algorithms dramatically.

A restricted domain text-to-speech system oriented to a weatherforecast application is presented. This TTS system is embed-ded in a multimedia interactive service accessible from differ-ent media, such as TV, Internet and mobile devices. The re-quirements of this application give rise to several particularitiesin the design and implementation of the TTS system, which arediscussed throughout this paper. Several tests have been con-ducted to analyze the TTS system in terms of performance andspeech quality.