Engineering Leadership Education: A Review of Best Practices
Notice bibliographique
Résumé
Abstract Engineering Leadership Education: A Review of North American Best PracticesAbstractModern society is built by engineering accomplishments that seemed impossible only a coupledecades ago. As society thrives forward and a new generation of engineers is just around thecorner, the question becomes: are we properly educating our engineers for the future? In the past,intellectually talented engineers with strong technical skills were sufficient for the needs ofsociety. However, in the 21st century engineers are now working in the corporate world,disconnected from the “hands-on” aspect of engineering. Professional skills such as leadershiphave become critical for graduating engineers entering the workforce.The MIT Engineering Leadership highlights curriculum changes specific to leadership in their2009 report, Engineering leadership education: A snapshot review of international goodpractice.1 The report looked at 40 worldwide engineering leadership education programs. Onemain finding was that there is “a surprising dearth of resources, expertise, and formal networkscurrently available in the field of engineering leadership education.” This report was publishedfive years ago, and although the field has gained significant headway since then, there is still anevident lack of information and resources in engineering leadership education.This paper will present a review of North American engineering leadership education programs(including institutions, specializations, and courses) to provide an overview of current offerings,to compare variations in approaches, and to summarize examples of best practice.Initially, a literature review will assist in providing a clear definition of leadership, the qualitiesof a leader, the leadership process, and an overview of leadership theories. Next, the idea ofeducating and teaching others leadership will be explored, specifically within a context of 21stcentury engineering. In order to understand the effectiveness of teaching leadership, it will benecessary to address the issue of how to measure leadership. Key quantitative and qualitativemeasurement criteria of leadership will be defined, followed by an analysis of assessment toolsthat have been used in practice. The research up to here will provide a solid background as thegroundwork for the final section.The final section will summarize case studies of engineering institutions that currently haveleadership education programs, including dedicated departments, specializations or certifications,and individual courses or workshops. Each case will be compared against similar cases todetermine consistencies and variations within each program type. The results from this reviewwill provide an understanding of current offerings in engineering leadership and summarize bestpractices. Insight will be gained on the progress of the field of engineering leadership education,and areas that remain sparse will be highlighted.[1] Graham, R., Crawley, E., & Mendelsohn, B. (2009). Engineering leadership education: A snapshot review of internationalgood practice. White paper sponsored by the Bernard M. Gordon-MIT Engineering Leadership Program. Retrieved fromhttp://www.rhgraham.org/RHG/Recent_publications_files/ELE%20White%20Paper-102109_1.pdf
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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 | 0,023 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,028 | 0,032 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».