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Breaking Bad Behavior: Immersive Training of Class Room Management
Marc Erich Latoschik∗ Jean-Luc Lugrin† Michael Habel‡ Daniel Roth§ Christian Seufert¶ Silke Grafe‖
HCI Group & School Pedagogics Group, University of Wurzburg
Figure 1: Left: Instructor interface to control the simulation. It supports different 3D views of the classroom and the trainee performance(left), a virtual student control board including a seating plan to drag-and-drop behaviors to individual students (right), a next instructionprompter (top) as well as widgets for trainee evaluation and camera control (bottom). Right: immersive view of the trainee interface.
Abstract
This article presents a fully immersive portable low-cost Virtual Re-ality system to train classroom management skills. An instructorcontrols the simulation of a virtual classroom populated with 24semi-autonomous virtual agents via a desktop-based graphical userinterface (GUI). The GUI provides behavior control and traineeevaluation widgets alongside a non-immersive view of the class andthe trainee. The trainee’s interface uses an Head-Mounted Display(HMD) and earphones for output. A depth camera and the HMD’sbuilt-in motion sensors are used for tracking the trainee and foravatar animation. An initial evaluation of both interfaces confirmsthe system’s usefulness, specifically its capability to successfullysimulate critical aspects of classroom management.
Keywords: Virtual Reality Training, Class Room Management,Virtual Agent Interaction, Student Simulation
Concepts: •Computing methodologies → Virtual reality;•Software and its engineering → Virtual worlds training simu-lations;
1 Introduction
A central skill for teachers and tutors is to learn how to handle, stop,and avoid bad or disrupting behaviors in face-to-face one-to-manyteaching situations to efficiently reach the teaching goals [Emmerand Stough 2001; Brouwers and Tomic 1999]. Competencies about
∗e-mail:[email protected]†e-mail:[email protected]‡e-mail:[email protected]§e-mail:[email protected]¶e-mail:[email protected]‖e-mail:[email protected]
Permission to make digital or hard copies of part or all of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contactthe owner/author(s). c© 2016 Copyright held by the owner/author(s).VRST ’16, November 02-04, 2016, Garching bei Munchen, GermanyISBN: 978-1-4503-4491-3/16/11DOI: http://dx.doi.org/10.1145/2993369.2996308
how to establish order, engage students, or elicit their cooperationare the essential aspects of class room management (CRM) [Emmerand Stough 2001] during teacher-training [Kunter et al. 2015].
The Breaking Bad Behavior (B3) system is a Virtual Reality CRMtraining simulation. In contrast to [Hayes et al. 2013], B3 uses afully immersive virtual classroom scenario similar to [Rizzo et al.2005] and populated with 24 semi-autonomous virtual agents. Itsupports an instructor-trainee paradigm: The instructor controls thesimulation and evaluates the trainee performance via a desktop-based graphical user interface (GUI) (see Figure 1 left). The traineeenters the simulation using a Head-Mounted Display (HMD) andearphones (see Figures 1 right and 2). B3 includes a large varietyof avatar animations to enhance realism, an important factor for thesuccessful elicitation of emotional responses, i.e., stress, which isverified by measuring trainee’s physiological reactions as inspiredby [Meehan et al. 2002].
Figure 2: Setup of a typical training session using the B3 systemwith the instructor interface at the lower right including a feedbackstation for physiological measures (small touchpad) and the traineein the back of the room.
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2 System Description
Figure 3 illustrates the system architecture. The immersive in-terface is rendered by a server component running a master con-troller responsible for feedback generation, session management,and semi-autonomous behavior simulation. A blackboard commu-nicates agents’ behaviors and perceptions. Behaviors are organizedin behavior trees connected to a state machine. Behavior types wereadopted from the literature used for CRM training. The simulationstate is replicated on the instructor GUI client component.
The system uses the Unreal Engine 4.xTM. The trainee’s view isrendered to the Oculus Rift DK2 HMD and the trainee’s move-ments are captured by the Rift (head) and by the Microsoft Kinectv2 (body). Movement data is embedded into the main system by theKinect4Unreal plug in. The GUI as been developed using the Un-real Motion Graphics UI Designer (UMG). Physiological measuresare taken by the Wristband E4 from Empatica.
Figure 3: Overview of the B3 system. The client-server architec-ture replicates the simulation between the instructor and traineeinterfaces. See text for details.
3 Results and Conclusion
We have evaluated the system with various measures for anxiety,tense, energetic arousal, simulator sickness, behavior categories,presence, immersion, suspension of disbelief, intuitive use and taskload, technology acceptance, and performance. In general, user ac-ceptance for both, trainees and instructors was good and the instruc-tor interface was rated to be quite intuitive. A complete report onthe studies and findings is currently under review. Here we brieflyreport on the technical evaluation and online feedback of physio-logical data for stress-related measures.
We performed video-based measurements of the end-to-end latencyusing frame-counting to verify synchronous temporal visuomotoricstimulation. The average end-to-end latency between head move-ments and corresponding updates of the projected images was eval-uated to approximately 73 milliseconds (± SD 45). Measurementswere realized with videos recorded at 480 Hz with the Casio EX-ZR200 Camera at a resolution of 224 x 160. The overall systemdelivered an average frame rate of ≈ 75 frames per second for anaverage number of 400K triangles per frame.
Figure 4 illustrates the development of the electrodermal activityover the four phases of 10 typical sessions. In general, the record-
ing of the physiological measures worked reliable and first resultssupported successful elicitation of appropriate stress as expectedfrom bad behaviors in real classrooms. The immediate feedback ofthese measures using the instructor interface as depicted in Figure 2supports the performance evaluation task of the instructors.
The B3 system provides a portable low cost (≈e 4000) fully immer-sive VR system for training classroom management skills. The sys-tem provides an instructor-trainee paradigm and successfully simu-lates a virtual classroom with 24 different semi-autonomous virtualagents capable of displaying a variety of pupil behaviors includingtypical examples of bad or disruptive behaviors. Future work willconcentrate on enhanced student variety (including stereotypes),full-autonomous agents, and computer-supported trainee evaluationbased on voice and movement analysis.
Figure 4: Plot of the Development of electrodermal activity overthree phases of a session based on 5200 samples.
Acknowledgements
We would like to thank David Fernes, David Schraudt, Do-minik Lipp, Felix Brischwein, Jennifer Haefner, Johannes Lamm,Kristina Pedersen, Leon Burkhardt, Nicolas Maltry, Rene Fleischer,Samantha Straka, and Sebastian Slowik for their contribution.
References
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