Application of the Cognitive Apprenticeship Framework to a Middle School Robotics Camp ∗
D. Brian Larkins
Computer Science Coastal Carolina University P.O. Box 261954 Conway, SC 29528-6054
J. Christopher Moore
†
Laura R. Covington
Chemistry and Physics Coastal Carolina University
Chemistry and Physics Coastal Carolina University
[email protected]
[email protected]
[email protected] Louis J. Rubbo Chemistry and Physics Coastal Carolina University
[email protected] ABSTRACT
1.
This paper details the development and implementation of a summer robotics camp for middle school children. The Cognitive Apprenticeship (CA) model is used as the framework for developing the camp. Specifically, methods such as modeling, coaching, scaffolding, articulation, reflection, and exploration are integrated throughout the camp structure. Activities include the use of an engineering notebook, debugging logs, and various cognitive challenges, all supervised by a team of expert mentors. During the two-week summer camp, participants realized a positive shift in attitudes towards science, developed common engineering design skills, and showed increased proficiency in the reasoning pattern of isolation of variables. The CA framework, in conjunction with robotics are an excellent way to build interest in STEM and develop skills in engineering, science, and computational thinking.
A troubling trend in education is the lack of proficiency with K-12 students in STEM (Science, Technology, Engineering, and Math) subjects. Even more concerning is that the problem is not limited to aptitude, but also a lack of interest in STEM fields. Building interest and improving student proficiency can be accomplished by establishing a personal connection with STEM ideas and activities at an early age [7]. Early intervention through out-of-school programs can foster a lifelong interest in science and technology for all students and is crucial to the long-term success of struggling students [4]. Early intervention and full immersion in STEM-based activities is of critical importance. At this stage of development, immersion in a guided-inquiry environment is necessary but not sufficient in developing scientific reasoning. Research has shown that explicit instruction is necessary to successfully develop reasoning skills for all age levels [6, 11]. In this paper, we describe the application of the cognitive apprenticeship (CA) model applied to Robocamp, a summer camp program for middle school students. This program provides students a fully immersive experience at an early age with three principal objectives: a) improving their perception of STEM subjects b) developing scientific reasoning skills, and c) to consider the pursuit of a career in a STEM-related field. The Robocamp program is designed as two weeks of half-day sessions in which middle school students are introduced to a robotics curriculum and learn how to construct robotic hardware and software systems to solve “missions”. The Robocamp program uses LEGO Mindstorms educational kits in conjunction with the Carnegie Mellon University Robotics Academy curriculum to motivate the application of STEM-related tasks and ideas. Throughout the course of the robotics camp, students construct a variety of different Mindstorms robots and software programs to solve a set of problems related to terraforming distant planets. The robots and terraforming setting provide the backdrop for teaching topics such as design, problem solving and teamwork in addition to traditional STEM topics such as math, science, and technology using the cognitive apprenticeship approach.
Categories and Subject Descriptors K.3.2 [Computers and Education]: Computer and Information Science Education
General Terms Design, Human Factors
Keywords Cognitive Apprenticeship, Education, Robotics, K-12, STEM ∗Corresponding author. †Undergraduate student.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SIGCSE’13, March 6–9, 2013, Denver, Colorado, USA. Copyright 2013 ACM 978-1-4503-1775-7/13/03 ...$15.00.
INTRODUCTION
Our approach is based on the following insights: First, cognitive apprenticeship, being a recent instructional design model, is well suited to provide early intervention and full immersion for middle school students. Second, that using affordable educational robotics systems provides a natural problem domain in which to apply cognitive apprenticeship. This work makes the following contributions: First, we describe how CA can be applied to teaching scientific reasoning and computational thinking to middle school students. Second, we provide a set of techniques and activities that can be used in conjunction with this approach. Lastly, we demonstrate that upon completion of the camp, students leave with an improved perception of science, math, and technology, as well as a more positive opinion of pursuing a STEM-related career.
2.
BACKGROUND
Situated cognition theory postulates that all knowledge is situated in actions that occur within cultural, social, and physical contexts [2]. The “Zone of Proximal Development” (ZPD) construct introduced by Vygotsky is defined as the difference between what a learner can do on their own as opposed what he or she can do with help (i.e. in a social/cultural context) [10]. It is this area, where situated learning approaches such as cognitive apprenticeship can be useful in increasing the abilities of the learner. Cognitive apprenticeship borrows from traditional apprenticeship as an applied teaching technique for learners constrained to classroom environments. The application of cognitive apprenticeship is focused on improving proficiency and ability in an assisted learning context. Expanding the abilities of the learner in an assisted setting leads to an increase of their abilities in an unaided setting [3]. The application of CA has been studied in the context of undergraduate CS education, but with different objectives than increasing STEM perception and proficiency [8]. The principal teaching methods of cognitive apprenticeship are summarized as follows [1]: Modeling: This technique involves an expert within the subject domain explicitly demonstrating a task to a student observer with the intention that the learner is able to build a conceptual model for the task at hand. Implicit processes are exposed so that students can observe and understand the rationale for the problem-solving process. Coaching: The master observes students in the process of trying to perform a task and gives them individual feedback and can offer assistance at critical moments. Students are actively involved in the problem-solving process and are required to integrate subskills and conceptual knowledge. Scaffolding: The expert assists students in performing a task, some parts involving skills that are beyond the student’s abilities. Assistance is slowly withdrawn (fading) as the learner is able to manage more of the task on their own. Scaffolding helps students develop self-awareness and selfcorrection skills allowing them to be less dependent on external help. Articulation: This method requires learners to think about their own actions and explain them to others, making their knowledge explicit. This allows students to reorganize their knowledge and generalize its application to related problem domains.
Reflection: Students reflect on their own performance in solving a problem through analysis and deconstruction. Students can increase their self-awareness of knowledge and compare their own understanding with that of others. Students can also compare their own performance with that of an expert and adjust their own conceptual model accordingly. Exploration: In exploration, students investigate new methods, strategies, and test new hypotheses by exploring the problem domain. Students can set their own goals and develop their own testing strategies, all of which fosters independent learning. The first three methods (modeling, coaching, and scaffolding) are the principal techniques for CA. They are designed to help students construct a conceptual model for the problem domain and develop a set of cognitive skills through observation and practice. Both articulation and reflection serve to internalize their observations and experience, as well as aid in integrating new knowledge and problem-solving skills. Lastly, exploration fosters independence and encourages autonomous problem formulations and solutions.
3. 3.1
ROBOCAMP Overview
The Robocamp program is delivered over a two-week period with 3-4 hours of instruction each day. The camp was developed around a theme of terraforming foreign planets through the use of autonomous robots to accomplish this objective. During the first day of camp, students construct a basic LEGO Mindstorms robot configuration and program it to perform some simple tasks. The remainder of the camp is broken down into two parts, a set of four Moon Missions and the Pantheon. The Moon Missions span the first week of camp, where students are tasked with outfitting their robots with sensors and actuators to complete four specified tasks (such as collecting objects on the playfield, etc.). During the second week of camp (Pantheon), students compete to complete as many tasks as possible by the end of the camp. Tasks vary in difficulty, with more difficult challenges being worth more points in the competition. The challenges involve maneuvering a LEGO Mindstorms robot around a 4 × 8 foot game board containing a number of obstacles and objectives. For example, one challenge might be to have the robot leave the starting position, follow a blue line to a “frozen lunar lake”, then to insert a “water extraction pipeline” into it. The game board configuration and challenges were provided as part of the CMU curriculum. Each day of camp consisted of several activities. Early in the week, mentors would use the beginning of the camp day to demonstrate new sensors, their capabilities and limits, as well as how to use them in the Mindstorms software environment. Later in the camp, this time is replaced with modules on terraforming and space exploration with the aim to increase science literacy, interest in STEM topics, and to connect camp activities with actual scientific challenges. The camp was comprised of 27 middle school students assigned to groups of 2-3 students each. Participating students were from a diverse range of schools in the Horry County School District. There were a total of seven mentors for
Figure 1: Picture of one of the authors (Louis Rubbo, left) demonstrating a new technique to students.
the camp, three faculty mentors and four upper-level undergraduate assistants. Typically six mentors were available to assist the approximately 10 groups every day.
3.2
Engineering Notebooks
Each group of students were given an Engineering Notebook to record their designs and problem-solving processes over the course of the camp. Every task (for both the warmup Moon Missions and the Pantheon competition) has its own section in the notebook. For a given task, the engineering notebook contains a detailed description of the problem to be solved, a diagram that illustrates the problem visually, and a scoring rubric specifying the points awarded for each component subtask. Additional pages provide space for students to brainstorm possible solutions, sketch out ideas and strategies, and to record a description of their final design. During the build/test phase, students record the problems that they encounter in a debugging log, along with a short description of how they resolved the issue.
4.
ROBOTICS VIA COGNITIVE APPRENTICESHIP
Over the course of the two-week period, students learn problem-solving skills, computational thinking, and scientific reasoning using cognitive apprenticeship techniques. The CA methods described above were integrated into a variety of activities throughout Robocamp. Modeling Activities: Each day begins with the entire group observing a mentor demonstrating a new technique that can be used with the Mindstorms robots to solve a challenge. Different sessions cover light and touch sensors, dead reckoning and navigation, actuator control, etc. Figure 1 shows an example of one of the authors modeling an approach to a challenge based on dead reckoning and discussing some of the problems commonly associated with this type of solution. A common task for robots is line following, where the robot uses a light sensor to detect the presence of a contrasting line on the floor. The program idiom used to per-
Figure 2: Picture of an undergraduate student mentor (Laura Covington, left) actively coaching a group through a difficult challenge.
form this task is to loop between two states: (no-line → turn-left) and (see-line → turn-right). This approach is simple to implement, but the insight of having the robot simply oscillate between the two states is difficult for students new to robotics and programming. By demonstrating the basic line following algorithm, students are then able to construct a conceptual model for themselves and adapt the approach for their own purposes. Additional modeling happens within each group throughout the remainder of the day. When a group becomes overwhelmed by a problem, a mentor works with them directly. Often times, modeling occurs as the mentor simply describes their own thought process aloud, rather than through explicit demonstration. This exposes techniques such as costbenefit analysis and allows students to understand the reasoning and rationale behind decision making. Coaching Activities: Students spend the majority of their time in Robocamp working in their group on solving either the Moon Missions or Pantheon challenges. During this time, mentors are in continuous rotation checking in with each group and observing their progress. As shown in Figure 2, mentors help the students work through difficult challenges conceptually and pragmatically by employing inquiry-based learning methods. This approach keeps the students cognitively active in the problem-solving process, providing confidence while simultaneously building problem decomposition and reasoning skills. After camp participants leave for the day, mentors discuss their observations made throughout the camp session. This allows mentors to understand each individual student and to develop a specific strategy for coaching each group throughout the entire camp. Aside from the direct coaching during the robot missions, other coaching activities are integrated into the morning sessions involving the entire camp. The students are presented with a goal and a proposed “solution” for solving the problem. The proposed solution contains one or more errors within the program, illustrating a specific logic flaw or nuance which is easy to overlook by inspection. For example, early in the camp students are shown a pro-
gram that is intended to have the robot navigate in a square pattern. The turns are intentionally programmed to an angle slightly larger than 90◦ . When demonstrated, the robot appears to start off behaving correctly, but after a few repetitions is clearly not tracking a square path. Students then analyze the program and are coached by mentors to develop a hypothesis for the incorrect behavior (error propagation) as well as a debugging scheme (unit testing) to test the validity of their hypothesis. Scaffolding Activities: Since many of the students have never used or programmed robots prior to participating in Robocamp, early activities are heavily scaffolded. Some scaffolding is provided by LEGO directly, through detailed instructions on how to construct a basic robot along with a large number of tutorials and examples contained within the Mindstorms software. Additionally, the CMU Robotics Academy curriculum also provides mission specifications and game board configuration, along with more tutorials and sample programs. Aside from externally provided materials, the engineering notebook is a mechanism designed to help students structure the problem-solving process. The notebook provides a place for students to work through the process of completing a mission in a methodical manner and encourages the sequencing of sub-steps when constructing a solution. Some group demonstrations are scaffolded to develop intuition and assist with the construction of a conceptual model. An example of this is with the introduction of sensors. When introducing the ultrasonic sensor (used for range determination), a student is selected to “be a robot” and is tasked with walking a fixed distance using only input from a sensor. Walking too far ends in the destruction of the robot by means of lava, acid, or similar device. The student is then blindfolded and allowed to move while only receiving perceptual input from the “sensor”. The sensor input is provided verbally as the distance from the goal. The participating student steps normally at first, but slows down as the range becomes closer to zero, to avoid stepping off the edge. Once students understand the robot as being blindfolded, save for its sensors, they intuitively understand many of the challenges involved in navigation and perception. Articulation Activities: One of the key activities in having groups work to solve a task is that each individual participant must communicate their own ideas and plans to the other group members. Throughout the camp, much of a groups success or failure can be directly attributed to the ability of its members to communicate effectively. Groups with poor communication dynamics receive additional coaching to help individuals make their knowledge concrete and sharable with others. In some cases, students need to be reassigned to alternate groups with a more compatible communication dynamic. At the end of Robocamp, there is a graduation ceremony and an opportunity for students to demonstrate their robot to their parents. As with the group work, this forces each participant to explain their understanding and reasoning to others, allowing them to recast and generalize their knowledge for a different audience. Beyond explicit articulation via communication, the engineering notebook is an invaluable tool. One aspect of the engineering notebook is to provide a structured process to solve problems, another intention is that students consider
their actions and provide a document that can be shared with group members or mentors. The documentation provided by the notebook was especially useful if participants missed a day of camp, for example. Reflection Activities: Students reflect on their understanding and performance as they progress through the mission campaigns. In addition to working in their groups throughout the problem solving process, they also use the engineering notebook to annotate knowledge gained during each task. Each engineering notebook contains a log which tracks their steps in debugging their robot for each mission. By making a written record of this process, students are able to analyze and generalize both problems and solutions for a variety of tasks. Additionally, groups are also able to observe each other and their relative progress. This permits creative ideas to be shared amongst groups and promotes a self-awareness of a student’s abilities relative to others. Exploration Activities: Throughout the first week of Robocamp, students follow a more prescribed program, designed to provide a foundation for completing tasks with the LEGO robot and Mindstorms software. The second week of Robocamp, however, is far less structured and provides more freedom to experiment and innovate. The Pantheon missions are setup as a large set of tasks of variable difficulty and points. Students compete to maximize the points obtained within the fixed time remaining in the camp. This allows some groups to select difficult challenges that require more time investment, but have a larger point payoff. Alternatively, weaker groups may choose a strategy based on quantity of tasks completed, rather than difficulty. Students have freedom to work on tasks that utilize skills and concepts previously mastered (specialization) or to try and solve tasks that require new skills and programming techniques (generalization). The Pantheon activities foster both self-confidence and independent learning.
5.
EVALUATION
A mixed-methods evaluation plan was designed and implemented for assessment of the camp goals. Specifically, we looked at quantitative gains in student perceptions of and interest in the various STEM areas using the STEM Semantic Survey (SSS) [9]. We also implemented a pre/post research design to investigate student use of “fair testing” via a common isolation of variables Piagetian task [5]. A qualitative evaluation of student engineering notebooks was used to asses the development of design and troubleshooting skills. The SSS measures student interest in five STEM areas: (1) science, (2) math, (3) engineering, (4) technology, and (5) interest in STEM careers more broadly. Figure 3 shows pre- and post-camp results from the SSS. Responses from a group (n = 43) of STEM professionals, as reported in [9], are also shown for comparison. For all areas, a score greater than 4 indicates a positive attitude, whereas a score less than 4 indicates a negative attitude toward the particular area. Our goal for this project was for students to approach attitudes consistent with STEM professionals [9]. As indicated in Figure 3, this goal was achieved. Interestingly, before the start of camp students expressed interest in engineering and technology at levels similar to STEM professionals. Furthermore, the average camper arrived with positive at-
Figure 3: Pre- and post-camp results from the STEM Semantic Survey. Responses from a group of STEM professionals are also shown for comparison. As evaluated, a score greater than 4 indicates a positive attitude, whereas a score less than 4 indicates a negative attitude, as indicated by the vertical dashed line.
titudes about all STEM areas. This is not particularly surprising, since the camp consisted of a self-selecting group and camp promotional materials clearly indicated a focus on engineering. No statistically significant shift (p>0.1) in student attitudes towards math or STEM careers was observed, though students arrived at camp with an already high interest in STEM careers. Student attitudes towards science demonstrated a positive shift significant at the 10% level, which may be attributed to the scientific content incorporated throughout the engineering tasks. Engineering notebooks were evaluated to determine if the camp was successful at developing common engineering design skills, such as articulation, rapid prototyping, design evaluation, and troubleshooting. Figure 4(a) shows a scanned image from one group’s engineering notebook during an early camp mission, whereas Figure 4(b) shows an excerpt from the same group’s notebook during a later camp mission. Comparison of the excerpts in Figure 4(a) and (b) demonstrate significant improvement in the student’s articulation of their design plan and goals for a particular mission. As camp progressed, we also noticed an increase in effective pre-design through the use of sketches, multiple design options, and evaluation of options based on certain criteria either expressed or implied. Figure 4(c) shows an excerpt from an engineering notebook during a late-camp mission where the group proposed and sketched three different design options for achieving a task and articulated a decision based on perceived constraints. Furthermore, student debugging logs (not shown) demonstrated increased sophisti-
Figure 4: Scanned images from a student group’s engineering notebook (a) during an early camp mission, and (b) during a later camp mission. (c) As camp progressed, students began to include more sketches and multiple design options.
cation with isolating and correcting problems in design and code as camp progressed. To assess development of “fair testing” use in the form of variable isolation, we implemented a pencil and paper test based on a common Piagetian task [5]. Figure 5(a) shows a drawing of three balls, a ramp, and a small block. Ball 1 and Ball 3 are the same size. Ball 2 is larger, however, both Ball 1 and Ball 2 have a weight of 5 units. Ball 3 has a weight of 10 units. Ball 1 is released from a low position on an inclined block of wood, as shown in the drawing. The ball hits the small block at the bottom of the ramp causing it to move a distance of 1 unit. The students are told that Ball 3 is then released from the top of the ramp and strikes the block. The block moves 2 units. The block is then replaced with another block having twice the density. Ball 1 is released from the top of the ramp and strikes the more dense block. The dense block moves only 1 unit. The students are asked if the result of the experiment shows that Ball 3 can move a target a greater distance than Ball 1. In order to earn credit for a correct response, the student must answer correctly and provide a coherent and correct explanation. Figure 5(b) shows the percent of the popula-
ing the Robocamp experience to a number of other schools in the Horry County School District. The Robocamp model is affordable, effective, and can be easily transferred to others, such as middle school teachers. We see the cognitive apprenticeship model as being applicable to both traditional education in middle schools, as well as rural schools and home schooling.
7.
ACKNOWLEDGMENTS
This work was supported by the South Carolina Space Grant Consortium under the Research and Education Awards Program (REAP). Partial funding was provided by the National Science Foundation (DMR #1104600) and the College of Science at CCU. We would also like to thank our student mentors and the participants of the CCU Robocamp 2012 program.
8. Figure 5: (a) Schematic of the set-up for the Piagetian task used to identify correct use of the isolation of variables reasoning pattern. Three different balls may strike a block after rolling down a ramp. Ball 1 and Ball 2 have the same mass of 5 units, but different diameters. Ball 1 and Ball 3 have the same diameter, but different masses. (b) Percent of the student population that correctly answered and demonstrated correct reasoning for the isolation of variables question.
tion that correctly answered the question and explained their reasoning both before the start of camp and after the completion of the camp. An increase significant at the 10% level was observed, suggesting emphasis on “fair testing” throughout the camp led to improvements in the scientific reasoning pattern of isolation of variables.
6.
CONCLUSIONS AND FUTURE WORK
The decline of both aptitude and interest in STEM subjects will have a dramatic impact on the ability of future generations to solve the many significant challenges that lie ahead. Addressing raw skill deficiencies in science and math is a daunting challenge in and of itself. This challenge is only compounded with increasing apathy in STEM professions and subjects. It is understood that addressing these two crucial problems is of critical import, not only at an early age – but also with techniques that work to develop the skills needed for success in STEM. To address these challenges, we have described the application of the cognitive apprenticeship model to change perceptions about STEM subjects and improve student ability for scientific reasoning. By providing an environment which is fully immersive and targets students at an early age, we have shown the CA approach to be successful in changing STEM perceptions. The approach described is based on the notion that the proximal development of participants can be improved by a combination of modeling, coaching, scaffolding, articulation, reflection, and exploration. This approach has been demonstrated to be successful both quantitatively and also qualitatively. Moving forward, we are currently in the process of bring-
REFERENCES
[1] J. S. Brown, A. Collins, and P. Duguid. Situated Cognition and the Culture of Learning. Educational Researcher, 18(1):32–42, Jan. 1989. [2] A. Collins, J. S. Brown, and A. Holum. Cognitive Apprenticeship: Making Thinking Visible. American Educator, 6:38–46, 1991. [3] A. Ghefaili. Cognitive Apprenticeship, Technology, and the Contextualization of Learning Environments. Journal of Educational Computing, Design, and Online Learning, 4, Fall 2003. [4] P. Lauer, M. Akiba, S. Wilkerson, Apthorp, H.S., D. Snow, and M. Martin-Glenn. Out-of-School-Time Programs: A Meta-Analysis of Effects for At-Risk Students. Review of Educational Research, 76:275–313, 2006. [5] A. Lawson. The development and validation of a classroom test of formal reasoning. Journal of Research in Science Teaching, 15(1):11–24, 1978. [6] W. Overton, editor. Reasoning, Necessity, and Logic: Developmental Perspectives. Lawrence Erlbaum Associates, 1990. [7] President’s Council of Advisors on Science and Technology. Prepare and Inspire: K-12 Education in Science, Technology, Engineering, and Math (STEM) For America’s Future, September 2010. Executive Office of the President. [8] A. Shabo, M. Guzdial, and J. Stasko. Computer Science Apprenticeship: Creating Support for Intermediate Computer Science Students. In Proceedings of the 1996 International Conference on Learning Sciences, ICLS ’96, pages 308–315. International Society of the Learning Sciences, 1996. [9] T. Tyler-Wood, G. Knezek, and R. Christensen. Instruments for Assessing Interest in STEM Content and Careers. Journal of Technology and Teacher Education, 18(2):341–363, 2010. [10] L. S. Vygotski˘ı. Mind in Society: Development of Higher Psychological Processes. Harvard University Press, 1978. [11] B. White and J. Frederiksen. Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students. Cognition and Instruction, 16(1):3–118, 1998.
