Engineering Leadership Education: A Review of Best Practices
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".