On the Mutuality of Agents and their Environments: Learning Ecology by Designing Robots and their Habitats in Elementary Grades

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On the Mutuality of Agents and their Environments: Learning Ecology by Designing Robots and their Habitats in Elementary Grades
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  !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+ 1 ON LEARNING ECOLOGY IN ELEMENTARY GRADES BY DESIGNING ROBOTIC ANIMALS AND THEIR HABITATS Gokul Krishnan Pratim Sengupta ++ Amanda Dickes Amy Voss Farris Mind, Matter & Media Lab Vanderbilt University – Peabody College Author Note: ++ Author for correspondence: Pratim@m3lab.org. Citation: !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+  !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+ 2 Introduction Complex systems involve multi-level phenomena in which phenomena at the aggregate level (e.g., formation and overall movement of a traffic jam) overall arise from simple, rule- based interactions between many individual actors or agents (e.g., cars moving forward and slowing down). Research has shown that in general, students of all ages find learning about complex systems challenging (Hmelo-Silver & Azevado, 2006, Jacobson & Wilensky, 2006; Sengupta & Wilensky, 2009, 2011). Ecology is a example of such a complex system. In the domain of ecology, students often struggle to understand key phenomena in an ecosystem, such as agent-environment relationships, and the relationships between structure and function of different elements in an ecosystem (for a review, see Catley, Lehrer, Reiser, 2005). In this design-based research study (Cobb et al., 2000) we present a novel educational robotics-based learning environment where elementary grade students used LEGO Mindstorms TM to learn about ecology by engaging in iterative design-based learning activities (Papert, 1980; Kolodner et al., 2003; Kafai & Ching, 2008). Students in our study engaged in two kinds of design tasks: construction and iterative refinement of robot-animals, and construction and refinement of the habitats in which these animals had to operate and survive by foraging for food and communicating with other animals. We will also present an analysis of the process of students’ conceptual development as they progressed through the activities. Theoretical Framework & Background The use of robotics in classrooms has shown to promote student’s design-based thinking and enhance their understanding of scientific and mathematical concepts (Druin & Hendler, 2000). Students who engage in the design and construction of robots actively engage in learning that involves iterative refinement of concepts and computational artifacts, while often working collaboratively with others (Chambers & Carbonaro, 2003; Penner, 2001). Researchers have shown that integrating programmable, “digital manipulatives” such as robotics into science instruction enhances students’ learning of science by 1) supporting student-driven scientific observation and inquiry, 2) leveraging, rather than discarding, students’ prior knowledge, 3)  productive interactions with peers and 4) setting scenes for students’ productive science talk (Ching & Kafai, 2001; Resnick, 1998).  Method Eleven students (aged 8-11) were recruited by email solicitation for an after school study held in a large metropolitan university in the southern USA. Classes were held during July, for three days a week, from 9 am to 3.45 pm every day. Students were randomly assigned to groups of two and three for the first activity, where we introduced them to the LEGO robotics kits and  programming, and then asked them to construct and program simple movements of a simple three-wheeled robot. In the next activity, students were re-assigned into two groups of five and six. Each group was asked to research online the behavior and habitat of an animal chosen by them. During this activity, students conducted the following activities: a) they refined their earlier designs of the robots to mimic animal behavior such as foraging for food; and b) they rapidly prototyped a drawing of the habitat that the robots would inhabit, and then iteratively  !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+ 3 constructed and designed the habitat using paper and Lego bricks. In the final phase, students were asked to design and program the robots so that they would communicate with each other while foraging for food, and to avoid danger. Throughout the design process, students worked in teams to iteratively design, build, program, and test their robots. The activities performed during each phase are outlined below in Table 1 and a sample mapping between Biology, Robotics and the Learning Activities are shown in Table 2. Data was collected through both mounted and hand held video cameras as well as collected student artifacts (programming code, robots). Student thinking was elicited and video-recorded by way of qualitative, semi-clinical interviews with the researchers. Phase Description of Activity Learning Goals I 1.   Students learn simple programming skills to manipulate the behavior of their robot. 2.   Students learn how to manipulate the  behavior of their robot by varying both the robot’s environment and some basic aspects of its design. 1.   Students learn about the iterative nature of design. Students learn to modify robot behavior through observing simple cause and effect relationships between program code and robot  behavior. II 1.   Students redesign and program their robots to mimic an animal of their choosing. 2.   Students create a model of a habitat their robot-animal would realistically inhabit. 3.   Students modify and refine their robot-animal and the habitat so that their robots can communicate and forage. 1.   Students develop strategies that allow the robots to communicate with each other and accomplish foraging tasks. 2.   Students learn about the iterative nature of design. 3.   Students learn to modify robot behavior through observing simple cause and effect relationships between program code and robot  behavior. Table 1:  Overview of Activities conducted during each phase of the designing process. Decision Point Biological Analogy LEGO Solution Habitat Solution Interview Snippets Food Sources Dung beetles feed on manure and cow dung. Red and Blue Balls Balls are covered in  brown paper and distributed around the habitat. We found there are dung beetles in Texas and scarabs in Egypt.  And they eat cow dung! The dung is going to be the balls. Foraging for food Once dung beetles find dung, they start rolling balls of it away. The male of some species has tarsi (claws) at the forelegs for  burrowing and  pushing the dung. Robotic claw arm that will open and close simulating grabbing and releasing. Food needs to be edible, i.e., size and texture of food needs to be “grabbable” The robot is going to have claws that pick up the dung. Dung beetles have jaws like pincers. The robot will do what dung beetles often times do, push the ball till its  goal and then bury it to eat.  !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+ 4 Commu-nication  between  bots A few species communicate  by stridulation (rub- bing body parts together to make sounds). Touch sensors attached to the robots. The robots will use the state of its touch sensor to decide whether to initiate communication. Elements in the habitat may trigger communication. This affects spatial locations of elements in the habitat, as well as color of different elements in the habitat The dung beetles communicate by rubbing against each other. Table 2:  A Sample Mapping Between Biology, Robotics and Learning Activities Analyses & Findings: Gradual Development of Students’ Understanding of Ecology Analysis of students’ interview responses revealed during the initial robot-animal design activity that both the groups focused on making their robots behave and look like human beings. In doing so, students spent a few hours investigating relationships between different sensors and the structures that need to be built to (physically) support them. Such anthropomorphic reasoning, we believe, played a productive role in introducing students to the basics of LEGO Mindstorms  programming, as well as the process of construction of a robot. In the next phase, when students  began refining their robot-animal to fit a particular ecosystem or habitat, we found that following Lehrer & Schauble (2004), a fruitful question for organizing initial instruction is, “Who lives here?” As they addressed this question, students began to associate organisms to globally described physical locations (e.g., certain types of beetles live in certain types of deserts, etc.), and identify certain attributes to be common and different among certain different locations of the same type (e.g., deserts in Egypt and New Mexico share commonalities and differences from the vantage point of particular organisms). As students continued refining their robot-animals and their habitats, we found that students developed a more nuanced sense of habitat. Interview responses during students’ habitat design and refinement revealed that initially, students conceived the environment primarily as a resource for satisfying the needs of one or more focal organisms. The relationship between the organism and the environment was first perceived as unidirectional, and the students’ primary attention was on the organism (the habitat is perceived as a passive background). However, once the students’ attention began to shift to survival, they increasingly noticed that particular qualities of physical space, climate, and time affected the survival of organisms or assemblages of organisms. In time, they began to regard organisms and their environments as interacting, and their mathematical descriptions of these interactions increased in complexity, as evident in the evolving complexity of the underlying programming in each robot. For example, students in one group first built ‘Babybot’ (Fig 1a), a small robot that simply moved randomly in small step sizes. The second iteration resulted in ‘Minibot’ (Fig 1b), a slightly larger robot, who would move forward, sense obstacles with a touch sensor, and then turn right. The most advanced iteration produced ‘BeasterBunny’ (Fig 1c), a large robot equipped with ultrasonic sensors to detect obstacles such as walls, wherein upon detection of an obstacle, it then initiates a turning routine to exit the obstacle and continue exploring. BeasterBunny also employed touch sensors to process local features- allowing it to avoid certain obstacles if detected. Table 3 depicts the challenges faced by students and their solutions to  progressively overcome them.  !"#$%&'&( *+( ,-&./01'( 2+( 3#45-$( 6+( 7 89$$:;'""#$( 6+ <=>?=( @'"4%A+ !" $%&'"(") *+,-,). /" *-%0%"1&'. 2'&3%4 5. 6%4()"(") 7,8,1(+ 9"(0&-4 9"3 :;%(' <&8(1&14=  2'0-" 0"-$-&1-B '1 1%- 6&&/'C D9&E-"-&4- 9E F'1#9&'C 6$$94#'1#9& 9E G-$-'"4% 9& ,4#-&4- H-'4%#&. <F6G,H =>?=A( I&B#'&'09C#$( IF+ 5 Problem Student Solutions Sample Student Responses Dung-Bots would navigate outside the habitat Build boundaries surrounding the habitat. Attach a touch sensor near the base of MiniBot and BeasterBunny, to detect the  presence of the boundaries. Program the lower touch sensor to accomplish “obstacle avoidance.” The touch sensor detects the obstacle (habitat boundary), which is then used to elicit an appropriate  behavior from the Dung-Bot. The robots will use the state of its touch sensor to decide whether to keep moving forward. So the robot won’t run of the habitat and go crazy. You don't want the robot to go where its not supposed to go. The robot will activate the touch sensor and the ultrasonic sensor to detect the boundaries. MiniBot would fall apart as it traversed the habitat Add more pieces and attachments to increase the structural integrity of MiniBot so that it would not break apart as it moved through the habitat. We made MiniBot sturdier because when it hit anything it would pop out. Sometimes it would bump something and the bolt would come undone and  stopped working. BeasterBunny could not  pick up the Dung Balls Lower the claws nearer to the ground to scoop the ball easier. To fix the claw, I also lowered the claws  so they hang close to the ground. Also they are tilted so that the ball will be caught easier.”  The robots would have trouble detecting the  pyramid, crashing into it. Also, touch sensors were mounted too low, hitting the rocks Build a boundary around the pyramid. The robot would turn away from the obstacle once the touch sensor is pressed. Raise the touch sensors on all the robots to avoid the rocks on the habitat but high enough to detect the boundaries surrounding the habitat and the boundaries surrounding the  pyramid.  Maybe we need to use a touch sensor that was higher up. That might not work because it might not hit the pyramid because it is slanting. The tire would hit the pyramid before the sensor does.  
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