Vanessa Echeverria

Presentation Skills Estimation Based on Video and Kinect Data Analysis

This paper identifies, by means of video and Kinect data, a set of predictors that estimate the presentation skills of 448 individual students. Two evaluation criteria were predicted: eye contact and posture and body language. Machine-learning evaluations resulted in models that predicted the performance level (good or poor) of the presenters with 68% and 63% of correctly classified instances, for eye contact and postures and body language criteria, respectively. Furthermore, the results suggest that certain features, such as arms movement and smoothness, provide high significance on predicting the level of development for presentation skills. The paper finishes with conclusions and related ideas for future work.

Multimodal Selfies: Designing a Multimodal Recording Device for Students in Traditional Classrooms

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.

Automatic Labeling of Forums Using Bloom’s Taxonomy

The labeling of discussion forums using the cognitive levels of Bloom’s taxonomy is a time-consuming and very expensive task due to the big amount of information that needs to be labeled and the need of an expert in the educational field for applying the taxonomy according to the messages of the forums. In this paper we present a framework in order to automatically label messages from discussion forums using the categories of Bloom’s taxonomy. Several models were created using three kind of machine learning approaches: linear, Rule-Based and combined classifiers. The models are evaluated using the accuracy, the F1-measure and the area under the ROC curve. Additionally, a statistical significance of the results is performed using a McNemar test in order to validate them. The results show that the combination of a linear classifier with a Rule-Based classifier yields very good and promising results for this difficult task.

Where Is the Nurse? Towards Automatically Visualising Meaningful Team Movement in Healthcare Education

Providing immediate, effective feedback on team and individual performance in healthcare simulations is a challenging task for educators, such is their complexity. Focusing on emergency procedures on patient manikins, our prior work has demonstrated the feasibility of using multimodal data capture and analysis to generate visualisations of student movement, talk and treatment actions. The limitation to date has been the need for manual steps in the analytic workflow. This paper documents how we have automated several key steps, using new technologies, which were piloted during a nursing simulation. Combining role-based nurses’ movement data with high fidelity manikin logs, we have implemented a zone-based classification model, and are able to automatically visualise movements within an emergency response team, providing the data needed to design near real-time feedback for both educators and students.

Multi-tabletop System to Support Collaborative Design Assessment

This paper presents a study that describes the design and implementation of a tabletop system for supporting collaborative design in the classroom. A case study and two experiments are presented in order to evaluate the usefulness of the proposed system for students and educators. Ten educators and fifteen students participated in the experiments. Findings show that the usefulness, as well as the easiness of the proposed system are perceived as good from both, students and educators. These results suggest that the proposed system does have potential to be used in other educational areas or as a baseline for similar approaches.