Robotics in Special Needs Education Marjo Virnes
Abstract
University of Joensuu
The purpose of this study is to explore the potential of robotics as an educational tool in special needs education. Qualitative case studies are used to increase knowledge about programmable LEGO NXT and Topobo robotics constructions kits in special needs education, and about the social robot and Topobo that are used in early childhood education when possible learning disabilities have not yet been diagnosed. This study aims to provide suggestions about how robotics might be used to recognize disabilities at an early stage of education and to compensate for them in learning.
Department of Computer Science and Statistics P.O. Box 111 80101 Joensuu, Finland
[email protected] University of California San Diego Institute for Neural Computation Machine Perception Laboratory 9500 Gilman Drive La Jolla, CA 93093
[email protected]
Keywords Special needs education, educational technology, robotics, programmable construction kit, social robot.
ACM Classification Keywords K3.1 Computer Uses in Education (e.g. Computerassisted instruction, collaborative learning)
Introduction
Copyright is held by the author/owner(s).
There are no technology curricula and few specific technological tools for special needs education in Finland, even though almost a third of all school children between the ages of 7 and 16 receive special needs education (Figure 1). Thus, more research and development in educational technology are needed to
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overcome the barriers to learning that these children currently encounter.
effective solutions to the problems of special-needs children. Robotics, in the form of programmable construction kits and social robots [2, 4, 5], could make as great a contribution to improving the quality of special needs education. Technologies of this kind could enable educators to recognize children’s individual needs at an early stage of education and to compensate for their diagnosed disabilities. Robotics could also empower special-needs children to experience success in the learning of those technical skills that are central to our technology-oriented society.
Research questions
Figure 1: In Finland 22 per cent of school children receive part-time special needs education and 8 per cent receive fulltime special needs education. [6]
The growing number of school children currently receiving special needs education reflects not only an increase in the prevalence of learning difficulties, but also the determination of the Finnish education authorities to make early intervention available to those who need it. Intervention in the early stages of education can strengthen a child’s ability to learn by providing tailor-made educational activities that are specifically designed to meet individual children’s needs. Such improvements may later become visible in the OECD’s PISA surveys [3]. The increasing number of special-needs children and the need for early intervention challenge teachers and researchers in this field to discover new and more
The purpose of this study is to explore some of the uses of robotics in special needs education. The following research questions are used to suggest problems that need to be solved in educational robotics and some of the ways in which robotics might be used to make special needs education more effective. What can robotics teach to children of different ages with various individual needs?
How should robotics be developed to meet children’s individual needs? What elements in robotics are critical for successful use in special needs education?
How could robotics improve special needs education? How can robotics compensate for learning disabilities? How can robotics support intervention in the early stage of education?
Research method Qualitative research and case studies are used as the research methodologies for exploring the topic. I
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deliberately chose a qualitative approach because it enables me to elucidate the special features of robotics in the context of special needs education and because it promotes a clearer understanding of the major issues that are current in the field. Because this research is open-ended by design, it provides a context for the emergence of unexpected incidents from the research data [1].
the University of California San Diego3, in 2008, with toddlers who play with a social robot (Figure 3).
Data collection and analysis The aim of qualitative case studies is to collect rich research data from multiple sources. Methods of data collection with such a methodology include for instance videotaping, questionnaires administered to teachers and parents, the researcher’s observations, and the interviewing of children and teachers.
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Figure 2: a) A six-year-old child explores malfunctions of a self-constructed and programmed four-legged Topobo robot. B) LEGO NXT robotics supports long-term work with 5th and 6thgrade special-needs children.
Preliminary findings The first phase of this study focused on children’s individual needs by working with children in elementary special needs education. The preliminary findings of that phase addressed the following issues:
The collection of rich, layered and descriptive data allows the researcher to answer the research questions by considering them from various points of view. I use constant comparison method [1] and features of grounded theory as the main methods in the data analysis.
Children’s individual needs offer a challenge to robotics to accommodate the different phases of the work. These challenges can be used as a resource in robot design.
Settings The first phase of the study was conducted at the University of Joensuu, Finland, from September 2005 to May 20071. The research participants were children between four and six years old who worked Topobo [4], and 5th and 6th grade special needs education children who worked with LEGO NXT2 and Topobo (Figure 2). The second phase of the study is being conducted at 1
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The Technologies for Children with Individual Needs Project. http://cs.joensuu.fi/etp/ Lego Mindstorms. http://mindstorms.lego.com
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Robotics as a learning tool can help children to overcome barriers to learning when it fulfills the following two conditions: (1) Robotics should be so designed that it can meet children’s individual interests. (2) Robotics should support a child-centered pedagogical model that emphasizes the child’s active role as a constructor and a creator of knowledge.
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University of California, http://mplab.ucsd.edu
Machine
Perception
Laboratory.
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Physical access to the robot affects the child’s sense of emotional ownership of and connection to the robot. These two factors together contribute toward the successful achievement of various learning goals, such as the feasibility of long-term work and collaborative interaction, in special needs education.
The recognition of learning disabilities at an early stage in a child’s education
Compensating for learning disabilities in education
Enriching and improving special needs education by opening out new approaches and topics for learning
The provision of resources that designers of educational technology can use specifically for the benefit of the kinds of learners defined in this study.
References [1] Bogdan, R. C., and Biklen, S. K. Qualitative research for education: An introduction to theories and methods. Pearson: the United States of America (2006). Figure 3. The social robot Asobo can improve early childhood education by stimulating new kinds of language learning.
Preliminary research findings confirm that robotics based on construction and programming can provide suitable learning tools for kindergarteners of between four and six years old and for children in elementary schools. These cohorts exclude severely autistic children and toddlers, who might also nevertheless benefit from the application of robot technologies in education. The assumption therefore is that these users could benefit from a social humanoid robot.
Expected contributions of the research to the IDC field Research findings from the case studies already undertaken suggest the feasibility of an interactive design that promotes sound pedagogical practices for special needs education. These preliminary research findings can be used in the following areas:
[2] Kärnä-Lin, E., Pihlainen-Bednarik, K., Sutinen, E., and Virnes, M. Can Robots Teach? Preliminary Results on Educational Robotics in Special Education. Proc. International Conference on Advanced Learning Technologies, IEEE Press (2006), 319-321. [3] OECD Programme for International Student Assessment (PISA). http://www.pisa.oecd.org/pages/ 0,2987,en_32252351_32235731_1_1_1_1_1,00.html [4] Raffle, H. S., Parkes, A. J., & Ishii, H. (2004). Topobo: A constructive assembly system with kinetic memory. Proceedings of the SGCHI conference on human factors in computing systems, Vienna, Austria, 647-654. [5] Tanaka, F., Cicourel, A., and Movellan, J. R. Socialization between Toddlers and Robots at an Early Childhood Education Center. Proc. of the National Academy of Sciences, 104(46), 17954-17958, 2007. [6] Statistics Finland. While the number of pupils in full-time education increased, the number part-time special education decreased. http://www.stat.fi/til/ erop/2006/erop_2006_2007-06-15_tie_001_en.html
