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Enregistrement W3041212750 · doi:10.18260/1-2--34182

Assessing the Effects of a Robotics Workshop with Draw-a-Robot Test

2020· article· en· W3041212750 sur OpenAlexfundno aff
Abhidipta Mallik, Pooneh Sabouri, Shramana Ghosh, Vikram Kapila

Notice bibliographique

Revue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Langueen
DomaineComputer Science
ThématiqueTeaching and Learning Programming
Établissements canadiensnon disponible
Organismes subventionnairesYork UniversityAmerican Society for Engineering EducationDirectorate for STEM EducationNew York Space Grant ConsortiumNational Science Foundation
Mots-clésRoboticsArtificial intelligenceWorkforceCurriculumRobotEducational roboticsGovernment (linguistics)Test (biology)EntertainmentComputer scienceEngineeringPsychologyPolitical sciencePedagogy

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,048
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,041

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,048
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0040,003
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0120,005

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.

Tête enseignante Opus0,059
Tête enseignante GPT0,300
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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