About

Hugo Gonzalez Villasanti is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. He holds a PhD and MS in Electrical and Computer Engineering from Ohio State University, and a BS in Electromechanical Engineering from Universidad Nacional de Asuncion, Paraguay. His research interests focus on control theory and engineering strategies in social systems to empower people and improve their well-being. He is associated with the Equitable Networks Laboratory and his work involves the application of controls, design, dynamics, and vibrations to social systems, aiming to develop strategies that enhance social equity and individual empowerment.

Research topics

• Computer science
• Psychology
• Developmental psychology
• Speech recognition
• Clinical psychology

Selected publications

• Characterization of the Spatial Use of a Classroom Environment Using Markov Chain Analysis

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior author

  Curriculum planning for many educators and educational researchers takes into account the use of the classroom environment. This is true across most age groups, but becomes even more important for early childhood learning for which the design of the space, the activities associated with a particular location, and the learning objectives are tightly coupled. Existing approaches to understand classroom usage are based on human-based observational methods and qualitative assessments. However, these methods may result in misallocation of the spatial usage when multiple students are engaged in different areas within the environment and the human observer may not be present. To provide a more quantitative approach, this work investigates the use of non-invasive wearable sensors and Markov chains to analyze proximity data collected for 8 days over a span of 4 months. Probabilistic maps representing the transition sequences between different location and task states for 7 users are presented. Variations between users and from day-to-day interactions are discussed. The Markov chains derived from these data illustrate that students preferred the snack area when the teacher was not engaged within the decision-making process, with a shift to drama time or circle time when the teacher was engaged with the students.

  PublisherDOI

• A multi-agent model of human psychomotor learning in educational spaces

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior author

  Control-based adaptive interventions are promising tools to promote equitable psychomotor skill development in classrooms. This work develops a novel multi-agent model tailored for such interventions, where students are represented as agents in a cooperative task involving simultaneous learning and goal achievement. The model’s low dimensionality facilitates validation, while its mathematical tractability enables analysis and control-based task design. Two students interacting with a ball-on-plate task are simulated to investigate the sensitivity of inequity and learning outcomes to diversity and collaboration. The results reveal that the outcomes depend nonlinearly on diversity and collaboration, suggesting the need for optimal design of learning tasks.

  PublisherDOI

• Agent-Based Modeling of Classroom Dynamics for Participatory Design

  2025-08-17

  articleSenior author

  Abstract Classrooms are dynamic spaces where student learning emerges from interactions among peers, teachers, and the environment. We present a computational framework based on agent-based modeling to explore how physical layout, peer influence, and teacher guidance affect equity in skill development. The model represents classroom elements as potential fields and updates each student’s skill state from daily interactions, using equity metrics inspired by distributive justice. Comparing a barrier-free layout with one featuring physical obstacles, the simulation shows that unobstructed environments promote better access to learning resources, peer and teacher interactions, and ultimately reduce skill disparities. This work lays the groundwork for a sociotechnical digital twin that uses real-time data, and its flexibility in defining skill and equity criteria enables educators and stakeholders to test and refine classroom designs collaboratively. In doing so, it offers the foundation for a transparent and adaptable tool aimed at optimizing classroom conditions so that every learner gains equitable opportunities to learn in their classroom.

  PublisherDOI

• Modeling Social Dynamics in Cyber-Physical-Social Classroom Systems

  2024-10-29

  articleOpen accessSenior author

  In this paper, we present an agent-based model (ABM) to study equitable interventions in cyber-physical-social systems (CPSIS) within educational environments. With the use of influence derived from physical and cognitive factors, we can capture the nuances of social interactions and peer learning. Simulation results demonstrate how different layouts and instructional strategies impact equity, providing valuable insights for educators aiming to optimize classroom environments for diverse learners. The model represents an explainable framework for participatory modeling and intervention design.

  PublisherOA PDFDOI

• Who Said What? An Automated Approach to Analyzing Speech in Preschool Classrooms

  arXiv (Cornell University) · 2024-01-14 · 1 citations

  preprintOpen access

  Young children spend substantial portions of their waking hours in noisy preschool classrooms. In these environments, children's vocal interactions with teachers are critical contributors to their language outcomes, but manually transcribing these interactions is prohibitive. Using audio from child- and teacher-worn recorders, we propose an automated framework that uses open source software both to classify speakers (ALICE) and to transcribe their utterances (Whisper). We compare results from our framework to those from a human expert for 110 minutes of classroom recordings, including 85 minutes from child-word microphones (n=4 children) and 25 minutes from teacher-worn microphones (n=2 teachers). The overall proportion of agreement, that is, the proportion of correctly classified teacher and child utterances, was .76, with an error-corrected kappa of .50 and a weighted F1 of .76. The word error rate for both teacher and child transcriptions was .15, meaning that 15% of words would need to be deleted, added, or changed to equate the Whisper and expert transcriptions. Moreover, speech features such as the mean length of utterances in words, the proportion of teacher and child utterances that were questions, and the proportion of utterances that were responded to within 2.5 seconds were similar when calculated separately from expert and automated transcriptions. The results suggest substantial progress in analyzing classroom speech that may support children's language development. Future research using natural language processing is under way to improve speaker classification and to analyze results from the application of the automated framework to a larger dataset containing classroom recordings from 13 children and 3 teachers observed on 17 occasions over one year.

  PublisherOA PDFDOI

• A Multidisciplinary Approach to Improving Police Interactions with Black Civilians

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Evaluating the accuracy of automated processing of child and adult language production in preschool classrooms

  Frontiers in Psychology · 2024-06-26 · 1 citations

  articleOpen access

  Young children's language and social development is influenced by the linguistic environment of their classrooms, including their interactions with teachers and peers. Measurement of the classroom linguistic environment typically relies on observational methods, often providing limited 'snapshots' of children's interactions, from which broad generalizations are made. Recent technological advances, including artificial intelligence, provide opportunities to capture children's interactions using continuous recordings representing much longer durations of time. The goal of the present study was to evaluate the accuracy of the Interaction Detection in Early Childhood Settings (IDEAS) system on 13 automated indices of language output using recordings collected from 19 children and three teachers over two weeks in an urban preschool classroom. The accuracy of language outputs processed via IDEAS were compared to ground truth via linear correlations and median absolute relative error. Findings indicate high correlations between IDEAS and ground truth data on measures of teacher and child speech, and relatively low error rates on the majority of IDEAS language output measures. Study findings indicate that IDEAS may provide a useful measurement tool for advancing knowledge about children's classroom experiences and their role in shaping development.

  PublisherOA PDFDOI

• Accuracy of Automatic Processing of Speech-Language Pathologist and Child Talk During School-Based Therapy Sessions

  Journal of Speech Language and Hearing Research · 2024-07-17 · 6 citations

  article

  PURPOSE: This study examines the accuracy of Interaction Detection in Early Childhood Settings (IDEAS), a program that automatically transcribes audio files and estimates linguistic units relevant to speech-language therapy, including part-of-speech units that represent features of language complexity, such as adjectives and coordinating conjunctions. METHOD: measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy). RESULTS: measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy. CONCLUSIONS: IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children's language growth.

  PublisherDOI

• Classroom Sensing Tools: Revolutionizing Classroom-Based Research in the 21st Century

  Topics in Early Childhood Special Education · 2024-01-11 · 9 citations

  article

  Sensing technologies that provide continuous, real-time information about teachers’ and students’ individual experiences are increasingly being applied to classroom-based research. Sensing technologies provide a possible alternative to costly and time-intensive in-person or hand-coded observations and have the potential to increase our present understanding of the vastly different experiences students within the same classroom often have. The goal of the present article is to provide an overview of sensing technologies, an explanation of how these technologies can be applied in early childhood classroom-based research, and examples of existing studies that have successfully implemented sensing technologies in the classroom environment.

  PublisherDOI

• Who Said what? An Automated Approach to Analyzing Speech in Preschool Classrooms

  2024-05-20 · 10 citations

  articleOpen access

  Young children spend substantial portions of their waking hours in noisy preschool classrooms. In these environments, children's vocal interactions with teachers are critical contributors to their language outcomes, but manually transcribing these interactions is prohibitive. Using audio from child- and teacher-worn recorders, we propose an automated framework that uses open source software both to classify speakers (ALICE) and to transcribe their utterances (Whisper). We compare results from our framework to those from a human expert for 110 minutes of classroom recordings, including 85 minutes from child-word microphones (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n}=4$</tex> children) and 25 minutes from teacher-worn microphones (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n}=2$</tex> teachers). The overall proportion of agreement, that is, the proportion of correctly classified teacher and child utterances, was. 76, with an error-corrected kappa of. 50 and a weighted F1 of. 76. The word error rate for both teacher and child transcriptions was. 15, meaning that 15% of words would need to be deleted, added, or changed to equate the Whisper and expert transcriptions. Moreover, speech features such as the mean length of utterances in words, the proportion of teacher and child utterances that were questions, and the proportion of utterances that were responded to within 2.5 seconds were similar when calculated separately from expert and automated transcriptions. The results suggest substantial progress in analyzing classroom speech that may support children's language development. Future research using natural language processing is under way to improve speaker classification and to analyze results from the application of the automated framework to a larger dataset containing classroom recordings from 13 children and 3 teachers observed on 17 occasions over one year.

  PublisherOA PDFDOI

Frequent coauthors

• Laura M. Justice

  16 shared

• Mary Beth Schmitt

  8 shared

• Jing Sun

  The Ohio State University

  8 shared

• Kevin M. Passino

  The Ohio State University

  7 shared

• Nan Xiao

  Pearson (United States)

  6 shared

• Leydi Johana Chaparro-Moreno

  6 shared

• Tiffany J. Foster

  The Ohio State University

  6 shared

• G. Logan Pelfrey

  The Ohio State University

  5 shared

Education

• Doctor of Philosophy, Electrical and Computer Engineering

  Ohio State University

  2019

• M. Sc., Electric and Computer Engineering

  Ohio State University

  2015

Awards & honors

• OSU Presidential Fellowship