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15 results for “Educational robotics”

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zenodo52/100

PE-HRI-temporal: A Multimodal Temporal Dataset in a robot mediated Collaborative Educational Setting

Please note that this dataset corresponds to the training data used in "Social robots as skilled ignorant peers for supporting learning "[7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).    This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.  In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:   PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:  <ul> <li> last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.  </li> <li> T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available. </li> <li> T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.  </li> <li> T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity </li> </ul> PE-HRI_behavioral_timeseries_w_labels.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:  <ol> <li>non-incremental</li> <li>incremental</li> </ol> A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:  <ul> <li> T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window). </li> <li> T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window). </li> <li> T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window). </li> <li> T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window). </li> <li> T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window). </li> <li> T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.  </li> <li> T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window). </li> <li> T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window). </li> <li> T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window). </li> <li> T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window). </li> <li> redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window). </li> <li> positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window). </li> <li> negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window). </li> <li> difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window). </li> <li> arousal/(_inc): The average value of arousal for the team in that window/(until that window). </li> <li> gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).  </li> <li> T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).  </li> <li> T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window). </li> <li> T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window). </li> </ul> Apart from these 52 values, within each window, we also indicate:  <ul> <li>team: The team to which the window belongs to.</li> <li>time_in_secs: Time in seconds until that window.</li> <li>window: The window number.</li> <li>normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team. </li> <li>cluster_labels: The cluster number associated with each time window in reference to the productive and non-productive clusters found in [3]</li> <li>PE_score: The Productive Engagement score in each window</li> </ul> Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5]. Please note that this dataset corresponds to the training data used in [7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).

opencc-by-4.0Oct 2021View details →

zenodo48/100

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Oct 2023View details →

zenodo40/100

Figure 2. Taxonomy of the educational technologies for children with ASC-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

learner (Perlin, 1997). MOSOCO is an emergent technology implemented in a smartphone called “Mobile Social Compass” (Escobedo et al., 2012). Six basic social skills are being encouraged by prompting the user to initiate social contact. The menu displays symbols for the basic social skills – eye contact, space and proximity, start an interaction, asking questions, sharing interests and finish an interaction. The MOSOCO application has turned out to be an extremely useful tool as an online prompt in starting, maintaining and finishing social interaction for both typical and autistic students, as well as to anyone that feels need for improving their social competence.

opencc-by-4.0Aug 2015View details →

zenodo40/100

Resources for the article "Investigating the role of educational robotics in formal mathematics education"

This repository contains the material required to reproduce the study looking to investigate the role of educational robotics in formal mathematics education for 15 year old students in the French speaking region of Switzerland. This includes : <ul> <li> Pedagogical content in the form of both teacher and student resources </li> <li> Data collection ressources (surveys and tests) </li> </ul> If you use any of the resources provided in this repository, please cite the following • The Zenodo repository, DOI: 10.5281/zenodo.4649842 • The corresponding article : Brender, J., El-Hamamsy, L., Bruno, B., Chessel-Lazzarotto, F., Zufferey, J.D., Mondada, F. (2021). Investigating the Role of Educational Robotics in Formal Mathematics Education: The Case of Geometry for 15-Year-Old Students. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds) Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science(), vol 12884. Springer, Cham. https://doi.org/10.1007/978-3-030-86436-1_6 • Licence : CC-BY

opencc-by-4.0Apr 2021View details →

zenodo36/100

Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings

# Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings  ## Background This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil.  In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil. The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked.  For more information about how these measures were collected, please refer to this thesis at:  https://doi.org/10.11606/T.55.2020.tde-31082020-093935 Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column.    ## Description: Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value). Deviations (integer): number of face deviations of the participant during the question answering identified by the system. EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system. NumberWord (integer): number of words comprised in the sentence the participant gave. SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers. Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds. True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions.     Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201 ## Creators  Daniel Tozadore: dtozadore@gmail.com Roseli Romero: rafrance@icmc.usp.br ## License:  [Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)

opencc-by-4.0May 2024View details →

zenodo36/100

Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"

Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08). The SLR covers papers in WoS and Scopus from 2011 to 2022.

opencc-by-4.0Jul 2023View details →

ClinicalTrials.gov36/100

Tracheostomy Robotics and Cutting-edge Health Education for Airway Safety

ClinicalTrials.gov study NCT06236542. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

zenodo32/100

Fostering Inclusivity and Equality in Early Childhood Education via Computational Thinking and Robotics: A Systematic Review

opencc-by-4.0May 2024View details →

zenodo32/100

STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING

<h1>AMBIENTES COEDUCATIVOS STEM QUE COMBINAN ROBÓTICA EDUCATIVA Y PENSAMIENTO COMPUTACIONAL </h1>   STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING       Resumen en español El presente artículo de investigación se enfoca en abordar las desigualdades de género en el ámbito educativo, especialmente en las disciplinas STEM, mediante el uso de la robótica educativa y el pensamiento computacional. Para ello se realiza una revisión sistemática de literatura, con el objetivo de analizar tendencias, desafíos y oportunidades en la implementación de estas estrategias en Colombia y su impacto en el cierre de brechas de género. Aunque no se encontró una revisión que sintetice la evidencia de estos temas de manera conjunta, el estudio actual busca llenar ese vacío y contribuir a intervenciones educativas futuras más equitativas. Palabras clave (en español): Educación STEM, Robótica educativa, Pensamiento computacional, Brechas de género, Vocaciones científicas. Resumen en inglés This research article focuses on addressing gender inequalities in education, especially in STEM disciplines, through the use of educational robotics and computational thinking. To this end, a systematic literature review is conducted, with the aim of analyzing trends, challenges and opportunities in the implementation of these strategies in Colombia and their impact on closing gender gaps. Although no review was found that synthesizes the evidence on these topics together, the current study seeks to fill this gap and contribute to more equitable future educational interventions. Palabras clave (en inglés): STEM education, educational robotics, Computational thinking, Gender gaps, Scientific vocations.

opencc-by-4.0Aug 2024View details →

ClinicalTrials.gov32/100

The Impact of Robot-assisted Digital Education on Prenatal Women's Health Literacy

ClinicalTrials.gov study NCT06999421. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov32/100

Surgical Skill Labs for Robotic Mastectomy and Educational Program Using a Surgical Guide by Artificial Intelligence

ClinicalTrials.gov study NCT06380803. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

AIR Support: Artificially Intelligent Robot (AIR) Support for Pediatric Asthma Education

ClinicalTrials.gov study NCT07387718. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

zenodo24/100

data set related to article Improving Executive Functions at School in Children With Special Needs by Educational Robotics

This record contains raw data related to article Improving Executive Functions at School in Children With Special Needs by Educational Robotics

opencc-by-4.0Sep 2020View details →

ClinicalTrials.gov24/100

Assessment of Educational Effect of Home-made Robotic Surgery Simulator for Novice Robotic Surgeons

ClinicalTrials.gov study NCT03067532. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov20/100

Effect of Using Educational Robots During Vaccination on Fear and Pain in Children

ClinicalTrials.gov study NCT06856395. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

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