Designing, Developing, and Implementing a Course on LEGO Robotics for Technology Teacher Education
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».