The Impact of Documenting Design Thinking, the Engineering Design Process Canvas, and Project Communication on Design Self-Efficacy of First-Year Students
Notice bibliographique
Résumé
This complete evidence-based practice paper describes a study of three design interventions and a survey conducted of first-year engineering students at New York University to understand the impact on their design self-efficacy.The research question addressed in this study is whether there is an impact of documenting the design thinking process, the engineering design process, and project communication on students' level of self-efficacy to solve engineering design problems.And if so, to what extent did students find value in using the documentation activities for enhancing their engineering design capabilities?The aim of this study is to identify the best strategies for improving first-year students' design skills that will help them succeed in future design projects.Many tools have been developed to improve engineering design skills of first year students like design thinking exercises, the Engineering Design Canvas, and strategies for communicating ideas.The evidence-based practice described in this study consists of in-class exercises for each of these tools which include 1) an IDEO design thinking worksheet at the beginning of the project, 2) the Engineering Design Canvas at the middle of the project, and 3) the Heitmeier Catechism design communication strategies at the end of the project.This study was conducted at New York University in the first-year multidisciplinary introductory engineering course General Engineering 1004 Introduction to Engineering and Design.Each semester, half of the 700 first-year students enroll in this course which requires all students to complete a multidisciplinary semester-long design project.The engineering design self-efficacy questionnaire developed in 2010 was used before and after to determine the impact of the three design exercises.In addition to the design self-efficacy instrument, open-ended questions were asked about students' feelings toward the design process.This study encompasses one semester with 300 first-year students in an introductory engineering course.The pre-and post-surveys take place before and after the first and last design intervention, respectively.Statistical analysis of the Likert responses to the engineering design self-efficacy questionnaire are used to compare before and after data to determine areas where the design interventions had the greatest impact.Other data collected included major, year, and the project type they completed to identify if other trends impacted their self-efficacy.The survey results indicate that students' design self-efficacy had statistically significant improvements in all areas except for motivation to select a possible design.In general, the motivation dimension of self-efficacy had the smallest practically significant increase.However, student self-efficacy for confidence and success increased for each step of the engineering design process.The anxiety dimension saw a statistically and practically significant decrease for each engineering design step.While the causation is limited by the course design project being completed between the pre-survey and post-survey, the qualitative results indicate that many students found the design interventions to clarify aspects of each engineering design process step.
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Comment cette classification a été obtenuedéplier
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 | 0,016 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».