Assessing the Effects of a Robotics Workshop with Draw-a-Robot Test
Bibliographic record
Abstract
Abstract Our modern technological age is witnessing the pervasive impact of technology on healthcare, transportation, education, commerce, and entertainment. Thus, there is great demand for a well-prepared STEM workforce. To address this need for a tech-savvy workforce, government, corporate, and education sectors are all focused on creating and offering innovative teaching, learning, and training opportunities for students at all levels. In this vein, our team has designed and conducted a summer robotics workshop to increase the robotics knowledge and technical and entrepreneurial skills of participants. This workshop was for a duration of four weeks with two weeks devoted to guided training and two weeks devoted to collaborative robotic projects. In summer 2019, the workshop was attended by 10 teachers and 22 students from 8 inner-city high schools. Each teacher was requested to bring two students. The objective of the workshop was to introduce participants to fundamental principles of robotics as well as hands-on experiences in designing and creating prototype robotics solutions for real-world problems. The expectation was that after attending the workshop the teachers will incorporate similar robotics activities in their curriculum at schools and their students would assist them in classroom implementations. As robots are becoming increasingly common in workplaces (e.g., factories, warehouses, hospitals, etc.) and homes (e.g., Roombas), everyone has some views about what robots are and what they can do. Perceptions of robots held by people may be stereotypical, with many misconceptions arising from movies, science fiction, and other media. In this study, we were interested to know workshop participants’ initial views about robots and their use and if and how their initial perceptions changed by the end of the workshop. To gather evidence to help answer these questions, we conducted a “draw a robot test”. In this test, the participants were asked to draw any robot in its environment and label its different parts. All responses were anonymous, however to allow matching of pre-/post-test responses from same respondents the participants labeled their drawings with unique self-assigned numeric codes. The test was held at the beginning of the workshop (pretest) and on the last day (posttest). We analyzed the types of the robots that participants drew and compared the labels that they used to describe the robots in the pre and posttests. Our preliminary findings show that, in both the pre and posttest, the teachers drew different types of robot such as humanoid, wheeled mobile, fixed base, insect like, etc. Moreover, their labels indicated that the robots would perform different types of function such as cleaning, delivery, construction, etc. Comparison of the pre and posttest show that teachers used more technical terms such as microcontroller, servos, gears, color sensor, ultrasonic sensor, etc., to characterize their robots. Specifically, eight teachers mentioned many relevant technical terms in their robot drawings in the posttest. Moreover, seven teachers in the posttest drew wheeled robots as compared to four teachers who drew wheeled robots in the pretest. We believe that these changes may have resulted from teachers’ experiences in building and working with wheeled manipulator robots. Further investigations are needed to determine how these changes in teachers’ understanding of robots may influence their approaches for introducing and teaching about robotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".