Assessing the Impact of an Autonomous Robotics Competition for STEM Education
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Bibliographic record
Abstract
AbstractRobotics competitions for K-12 students are popular, but are students really learning and improving their Science, Technology, Engineering, and Mathematics (STEM) scores through robotics competitions? If they are, how much more effective is learning through competitions than traditional classes? What is the best robotics competition model to maximize students' STEM learning? One robotics competition designed to promote the use of math and science is Robofest. Robofest is an autonomous robotics competition with some unique features for STEM education. An example is that students need to solve unknown problems on the day of the competition. The Robofest competition requires the use of mathematics and sensors which discourages dead reckoning. Results from 5th-12th graders who completed a STEM assessment before and after the Robofest competitions found students in the Robofest group showed improvement and achieved higher scores in math and science after the competition. These results suggest robotics competitions modeled after Robofest have the potential to improve STEM learning.IntroductionWe believe computer programming and robotics are powerful learning tools for children (Papert, 1980). Robots first appeared in U.S. classrooms for educational purposes more than 20 years ago (Bers & Portsmore, 2005; Cejka, Rogers & Portsmore, 2006; Chambers & Carbonaro, 2003; Groff & PomalazaRaez, 2001; Kolberg & Orlev, 2001; Whitman & Witherspoon, 2003). More recently, several informal learning environments have started to combine computers and robots through such programs as after-school, computerized, autonomous robotics programs and robotics competitions (Barker & Ansorge, 2007; Chung & Anneberg, 2003). Robotics competitions engage participants in fixed and open-ended activities, and as suggested by Fred Martin (2000), one of the inventors of the popular LEGO robotics platform, open-ended exhibitions might promote more creativity than fixed game competitions. Furthermore, the use of autonomous robotics in formal and informal learning environments improves math and science learning, as well as critical thinking and problem solving skills (Matson, DeLoach & Pauly, 2004; Robinson, 2005; Weiss, 2004; Ricca, Lulis & Bade, 2006; Wagner, 1998).The characteristics of robotics-based pedagogy provide at least the following five key advantages over traditional pedagogy in teaching the theory and practice of STEM: (1) integration of STEM topics in a multidisciplinary fashion, (2) efficient transformation of abstract concepts into concrete learning modules for students, (3) combination of STEM theory with its practice, (4) hands-on learning that is active and engaging, and (5) a highly enjoyable and motivating learning environment.Beginning in 2000 and continuing annually over the next fourteen years, we have utilized the robotics-based pedagogy through an autonomous robotics competition, Robofest (www.robofest.net), to teach STEM skills to over 12,000 pre-college students (Chung, 2011; Chung & Sverdlik, 2001; MacLennan, 2010). Robofest has become an international competition, engaging teams from 13 US States (Michigan, Ohio, New Hampshire, Texas, Florida, California, Washington, Missouri, Hawaii, Colorado, Indiana, Minnesota, and Louisiana), and 8 countries (Canada, Mexico, United Kingdom, South Korea, Singapore, France, India, and China).Goals and features of RobofestRobofest challenges student teams to design, build, and program autonomous robots that embrace and naturally associate with STEM components. The two ultimate goals of Robofest are:* Goal 1: Get students interested in STEM subjects and careers* Goal 2: Increase preparedness for successful college education by increasing knowledge of STEM topicsTo accomplish our goals effectively, we have introduced the following unique and innovative features into Robofest.Affordable for all studentsRobofest is one of the most affordable autonomous robotics competitions in the nation. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it