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Enregistrement W2512769302 · doi:10.18260/1-2--6082

Helping Students Learn To Organize And Manage A Design Project

2020· article· en· W2512769302 sur OpenAlexaboutno aff
R. N. Boyd, Prabal Dutta, K. Pugsley, John Demel, Audeen Fentiman

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueEngineering Education and Pedagogy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésQuarter (Canadian coin)Session (web analytics)Class (philosophy)State (computer science)Engineering educationMathematics educationEngineeringComputer scienceMathematicsArtificial intelligenceEngineering managementWorld Wide WebAlgorithmHistory

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1438 HELPING STUDENTS LEARN TO ORGANIZE AND MANAGE A DESIGN PROJECT A.W. Fentiman, J.T. Demel, R. Boyd, K. Pugsley, P. Dutta The Ohio State University Introduction As part of the NSF-sponsored Gateway Engineering Education Coalition program, some freshman engineering students at The Ohio State University participate in a three- or four-quarter integrated sequence of courses that culminates in a one-quarter team design project. Two groups of students have completed the team design project during the past year. The first group, consisting of students who were calculus-ready when they entered Ohio State in the fall of 1994, took the design course in Spring Quarter 1995 (their third quarter). The other group, students who were not calculus-ready when they entered Ohio State, took an additional quarter of math and physics courses before they began the design course in Autumn Quarter 1995 (their fourth quarter). In the design class, teams of four or five students are required to build and program a robot to negotiate a 4 ft x 9 ft course with a hill in it, picking up blocks placed at prescribed locations and carrying them into the finish area. Figure 1 is a diagram of the course. Points are awarded for each block brought to the finish area. Teams can earn extra points by transferring blocks to an elevated bonus zone beyond the finish area. The robots are tested, both individually and in head-to-head competitions, at the end of the ten-week quarter. Points earned in the individual runs and the head-to-head competition contribute to the team’s course grade. Other activities that are graded throughout the quarter include written and oral reports and laboratory exercises related to various robot subsystems. At the beginning of the design project, each team is required to prepare a detailed design schedule. The schedules are reviewed by the faculty and suggestions are made to help students prepare realistic schedules. Teams in the first pilot group (Spring 1995) were expected to follow their schedules, and faculty informally monitored their progress. However, no formal intermediate deadlines or milestones were set. All teams completed their robots in time for the competition, and all successfully carried at least one block into the finish area. But many of the teams worked nearly around the clock the day or two before the competition. Since the competition was near the end of the quarter when instructors in other courses were giving exams and other term projects were due, spending many hours on the design project had a negative effect on some students’ grades in other courses. It was clear that students needed more help in organizing and managing their design projects. During the second pilot course (Autumn 1995), students were given more guidance in project organization and management. The methods used to help students learn how to organize and manage their design projects and some conclusions about the students’ performance are presented in the remainder of this paper. Helping Students Organize a Project At the beginning of the team design project, students are asked to prepare a design schedule. A detailed, realistic schedule is crucial if the teams are to successfully complete their projects in the ten-week academic term. It is important that the students develop the schedule themselves so that they are forced to think about the tasks that must be done and their relationships to each other. However most students, particularly freshmen, have not had any experience with developing a schedule for a multi-task project and do not know how to begin. $iii’1996 ASEE Annual Conference Proceedings ) ‘.,+,~yy’:

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)0,003
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,069
Score d'incertitude au seuil0,231

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,012
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,001
Communication savante0,0070,004
Science ouverte0,0030,006
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0690,062

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 Opus0,054
Tête enseignante GPT0,297
Écart entre enseignants0,243 · 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'étudeSans objet
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

Citations6
Publié2020
Routes d'admission1
Résumé présentoui

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