Persona: Paz López, Félix de la
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Paz López
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Publicación Speech gestural interpretation by applying word representations in robotics(IOS Press, 2018-12-03) Almagro Cádiz, Mario; Paz López, Félix de la; Fresno Fernández, Víctor DiegoHuman-Robot Interaction (HRI) is a growing area of interest in Artificial Intelligence that aims to make interaction with robots more natural. In this sense, numerous research studies on verbal and visual interactions with robots have appeared. The present paper will focus on non-verbal communication and, more specifically, gestures related to speech, which is an open question. With the aim of developing this part of Human-Robot Interaction or HRI, a new architecture is proposed for the assignment of gestures to speech based on the analysis of semantic similarities. In this way, gestures will be intelligently selected using Natural Language Processing (NLP) techniques. The conditions for gesture selection will be determined from an assessment of the effectiveness of different language models in a lexical substitution task applied to gesture annotation. On the basis of this analysis, the aim is to compare models based on expert knowledge and statistical models generated from lexical learning.Publicación Q-CHAT-NAO: A robotic approach to autism screening in toddlers(Elsevier, 2021-06) Romero García, Rubén; Martínez Tomás, Rafael; Pozo Cabanillas, María del Pilar; Paz López, Félix de la; Sarriá Sánchez, María EncarnaciónThe use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human–robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction.Publicación Using LSTM to Identify Help Needs in Primary School Scratch Students(MDPI, 2023-11-30) Imbernón Cuadrado, Luis Eduardo; Manjarrés Riesco, Ángeles; Paz López, Félix de lafirst-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.Publicación Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience(IEEE, 2023-12-06) Orlando, Samantha; Gaudioso Vázquez, Elena; Paz López, Félix de laNowadays, there is an increasing interest in using different technologies, such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in classroom. To support teachers, learning environments with robotics tools should monitor the students’ interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students’ learning progress. In previous work, we presented integrated didactic educational environment (IDEE), an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students’ interactions with IDEE are stored and analyzed using the additive factor model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a conjunctive knowledge tracing model based on a hidden Markov model. In this article: first, we describe how the CKT model has been adapted to the pedagogical model of IDEE, second, we show that this model can identify the skills that each student masters, and thus, support teachers in identifying learning criticalities in students.Publicación Frequency variation analysis in neuronal cultures for stimulus response characterization(Springer, 2019-01-03) Val Calvo, Mikel; Alegre Cortés, Javier; Ferrández Vicente, José Manuel; Fernández Jover, Eduardo; Val Calvo, Inhar; Álvarez Sánchez, José Ramón; Paz López, Félix de laIn vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence nonlinear and non-stationary. The usual approach for this characterization has been the use of the post-stimulus time histogram (PSTH). PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pairs produces a response in the other but not in the rest of the electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse inter-spike intervals, in each electrode. Machine learning methods can then be applied to distinguish the electrode being stimulated from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applicationsPublicación Supporting Teachers to Monitor Student’s Learning Progress in an Educational Environment With Robotics Activities(IEEE, 2020-03-06) Orlando, Samantha; Gaudioso Vázquez, Elena; Paz López, Félix de laEducational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms R© educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students’ learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students’ performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students’ learning process.