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13th International Conference on computer Supported Collaborative learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded learning in Collaborative Settings, CSCL 2019

作者： Sharma, Kshitij Olsen, Jennifer Department of Computer Science Norwegian University of Science and Technology Norway Computer Human Interaction Lab in Learning and Instruction École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781732467231

To provide beneficial feedback to students during their collaboration, it is important to identify behaviors that are indicative of good collaboration. However, in a collaborative learning session, students engage in a range of behaviors and it can be difficult to indicate which of those behaviors correlate with higher outcomes. In this paper, we propose using Extreme Value Theory (EVT), a method that considers the data points in the tail (upper or lower) of the distribution, to analyse the relationship between collaborative process variables and outcome measures through insights derived from high impact, low-frequency events. Specifically, in this paper, we analyse the relationship between dual gaze patterns and outcome measures across two different datasets. In both datasets we found that students with lower outcomes had lower focus during the collaborative session. This paper provides a contribution by both introducing EVT as a viable method for analysing CSCL data as well as demonstrating the effectiveness of eyetracking as a collaborative indicator to use to adapt to in real-time. © ISLS.

关键词： Students

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When Positive Perception of the Robot Has No Effect on learning

When Positive Perception of the Robot Has No Effect on Learn...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Jauwairia Nasir Utku Norman Barbara Bruno Pierre Dillenbourg Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland MOBOTS group within the Biorobotics Laboratory (BioRob) Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

关键词： Conferences humanoid robots Collaboration Particle measurements Collaborative work Task analysis Robots

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AlloHaptic: Robot-Mediated Haptic Collaboration for learning Linear Functions*

AlloHaptic: Robot-Mediated Haptic Collaboration for Learning...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Hala Khodr Soheil Kianzad Wafa Johal Aditi Kothiyal Barbara Bruno Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Laboratory (CHILI) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Department of Computer Science University of British Columbia University of New South Wales Sydney Australia MOBOTS Group in BIOROB EPFL Lausanne

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

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Robot Analytics: What Do human-Robot interaction Traces Tell Us About learning?

Robot Analytics: What Do Human-Robot Interaction Traces Tell...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Jauwairia Nasir Utku Norman Wafa Johal Jennifer K. Olsen Sina Shahmoradi Pierre Dillenbourg Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland

In this paper, we propose that the data generated by educational robots can be better used by applying learning analytics methods and techniques which can lead to a deeper understanding of the learners' apprehension and behavior as well as refined guidelines for roboticists and improved interventions by the teachers. As a step towards this, we put forward analyzing behavior and task performance at team and/or individual levels by coupling robot data with the data from conventional methods of assessment through quizzes. Classifying learners/teams in the behavioral feature space with respect to the task performance gives insight into the behavior patterns relevant for high performance, which could be backed by feature ranking. As a use case, we present an open-ended learning activity using tangible haptic-enabled Cellulo robots in a classroom-level setting. The pilot study, spanning over approximately an hour, is conducted with 25 children in teams of two that are aged between 11-12. A linear separation is observed between the high and low performing teams where two of the behavioral features, namely number of distinct attempts and the visits to the destination, are found to be important. Although the pilot study in its current form has limitations, e.g. its low sample size, it contributes to highlighting the potential of the use of learning analytics in educational robotics.

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Augmented Robotics for Learners: A Case Study on Optics

Augmented Robotics for Learners: A Case Study on Optics

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Wafa Johal Olguta Robu Amaury Dame Stephane Magnenat Francesco Mondada Biorobotics Laboratory (BioRob) Computer-Human Interaction in Learning and Instruction (CHILI) Laboratory Game Technology Center ETH Zurich

In recent years, robots have been surfing on a trendy wave as standard devices for teaching programming. The tangibility of robotics platforms allows for collaborative and interactive learning. Moreover, with these robot platforms, we also observe the occurrence of a shift of visual attention from the screen (on which the programming is done) to the physical environments (i.e. the robot). In this paper, we describe an experiment aiming at studying the effect of using augmented reality (AR) representations of sensor data in a robotic learning activity. We designed an AR system able to display in real-time the data of the Infra-Red sensors of the Thymio robot. In order to evaluate the impact of AR on the learner's understanding on how these sensors worked, we designed a pedagogical lesson that can run with or without the AR rendering. Two different age groups of students participated in this between-subject experiment, counting a total of 74 children. The tests were the same for the experimental (AR) and control group (no AR). The exercises differed only through the use of AR. Our results show that AR was worth being used for younger groups dealing with difficult concepts. We discuss our findings and propose future works to establish guidelines for designing AR robotic learning sessions.

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Responsive Joint Attention in human-Robot interaction

Responsive Joint Attention in Human-Robot Interaction

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： André Pereira Catharine Oertel Leonor Fermoselle Joe Mendelson Joakim Gustafson Speech Music and Hearing Lab EECS KTH Royal Institute of Technology Stockholm Sweden Computer-Human Interaction Lab for Learning & Instruction at École Polytechnique Fédérale de Lausanne Switzerland

Joint attention has been shown to be not only crucial for human-human interaction but also human-robot interaction. Joint attention can help to make cooperation more efficient, support disambiguation in instances of uncertainty and make interactions appear more natural and familiar. In this paper, we present an autonomous gaze system that uses multimodal perception capabilities to model responsive joint attention mechanisms. We investigate the effects of our system on people's perception of a robot within a problem-solving task. Results from a user study suggest that responsive joint attention mechanisms evoke higher perceived feelings of social presence on scales that regard the direction of the robot's perception.

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Dual gaze as a proxy for collaboration in informal learning 12

Dual gaze as a proxy for collaboration in informal learning

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12th International Conference on computer Supported Collaborative learning - Making a Difference: Prioritizing Equity and Access in CSCL, CSCL 2017

作者： Sharma, Kshitij Leftheriotis, Ioannis Noor, Jama Giannakos, Michail Faculty of Business and Economics University of Lausanne Switzerland Computer Human Interaction in Learning and Instruction EPFL Switzerland NTNU Trondheim Norway

ISBN: (纸本)9780990355007

Interactive displays are increasingly employed in informal learning environments as a technology for enhancing students’ learning and engagement. Interactive displays allow students to collaborate and interact with the content in a natural and engaging manner. Despite the increased prevalence of interactive displays for learning, we know very little about how students collaborate in such settings and how this collaboration influences their performance. In this dual eye-tracking study, with 36 participants, a two-staged within-group experiment was conducted to investigate students’ collaboration and learning gains in an interactive display. The results show that collaboratively, pairs who have high gaze similarity have high learning outcomes. Individually, participants spending high proportions of time in acquiring the complementary information from images and textual parts of the learning material attain high learning outcomes. We show that the gaze is an effective proxy to cognitive mechanisms underlying collaboration not only in formal settings but also in informal learning scenarios. © ISLS.

关键词： Students

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Semi-Markov model for simulating MOOC students 9

Semi-Markov model for simulating MOOC students

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9th International Conference on Educational Data Mining, EDM 2016

作者： Faucon, Louis Kidziński, Lukasz Dillenbourg, Pierre Computer Human Interaction in Learning and Instruction École Polytechnique Fédérale de Lausanne Switzerland

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student’s activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students. © 2016 International Educational Data Mining Society. All rights reserved.

关键词： Students

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On generalizability of MOOC models

On generalizability of MOOC models

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9th International Conference on Educational Data Mining, EDM 2016

作者： Kidziński, Lukasz Sharma, Kshitij Boroujeni, Mina Shirvani Dillenbourg, Pierre Computer Human Interaction in Learning and Instruction École polytechnique fédérale de Lausanne Switzerland

The big data imposes the key problem of generalizability of the results. In the present contribution, we discuss statistical tools which can help to select variables adequate for target level of abstraction. We show that a model considered as over-fitted in one context can be accurate in another. We illustrate this notion with an example analysis experiment on the data from 13 university Massive Online Open Courses (MOOCs). We discuss statistical tools which can be helpful in the analysis of generalizability of MOOC models. © 2016 International Educational Data Mining Society. All rights reserved.

关键词： Data mining

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