Designing, Developing, and Implementing a Course on LEGO Robotics for Technology Teacher Education
Bibliographic record
Abstract
Within a constructivist philosophy of learning, teachers, as students, are introduced to different perspectives of teaching with robotic technology while immersed in what Papert called a constructionist environment. Robotics allows students to creatively explore computer programming, mechanical design and construction, problem solving, collaboration, physics, motion, music--all within an active, enjoyable, and nonthreatening setting. The theoretical motivation for integrating robotics into the teacher education program comes from Jonassen's (2000) argument that technology tools can be viewed as cognitive tools or that enhance the learning process. Students are given ownership for their learning within a constructionist environment and allowed to discover and make choices as they explore countless avenues for solving design challenges. Through the use of innovative LEGO[R] RoboLab[TM] technology, students learn various facets of problem solving while simultaneously mastering numerous mathematical and scientific concepts. This article describes a case study of a pilot teacher education course in robotic technology. The goal was to design and develop a course that provides current and prospective teachers with a solid understanding of robot design, construction, and programming--as well as a demonstration and understanding of teaching using constructionist pedagogical strategies. ********** A revolution is beginning in the field of robotics that sees various aspects of robotics research leaving the laboratory environment and moving out into the world. Recent new programmable robotic such as SONY's robotic dog or the LEGO MINDSTORMS robot construction kit are typical examples. As Hendler (2000) pointed out, such toys the very nature of the relationship between children and technologies ... children are no longer anchored to a PC on the desktop, but able to bring the technology into their everyday world (p. 2). This in turn poses a challenge to the educational community of how best to integrate these new technologies into our school environment. The evolution of approaches and methods for the application of technology to teaching and learning is inherently linked to the evolution of the technology itself. Witness the impact desktop computers have had on a child's school experience over the last 20 years and the important role they now play in education (Santrock, 2001). The use of robotics in education is a relatively new phenomenon (Miglino, Lund, & Cardaci, 1999). That being said, there appears to be some evidence to indicate that robotics, as a classroom-teaching tool, can help promote student problem solving at many levels of education (Druin & Hendler, 2000; Thangiah & Joshi, 1997; Wagner, 1998). This article will present the theoretical and applied rationale for integrating robotics into a teacher education course in technology, describe a pilot course at the University of Alberta, student reflections on the course, and possible curriculum linkages for robotics. THEORETICAL AND APPLIED MOTIVATION FOR ROBOTICS Essentially one seeks to answer the question; why integrate technology, in the form of robotics, into the teacher education process? From a more general perspective there are many reasons to use technology in teacher education. Underlying almost all of these reasons is the notion that technology, if employed effectively, can positively impact the teaching process and subsequently either change or enhance the learning process (Papert; 1980; Logan, 1995). Jonassen (2000) makes a compelling argument for using computer technologies as in education in contrast to using computer technologies as a vehicle to deliver instructional material. The theoretical motivation for including robotics in teaching is grounded on Jonassen's notion that Mindtools can indeed change and enhance the learning process in education. The Mindtools perspective views the individual and computer in a joint-problem-solving-system or intellectual partnership such that the individual's problem solving ability and critical thinking skills are developed or amplified beyond the level that could be achieved without such a partnership (Logan, 1995; Orhun, 1995; Pea, 1985; Penner, 2001; Salomon, Perkins, & Globerson, 1991). …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".