THE ASSESSMENT OF LEADERSHIP OUTCOMES IN CAPSTONE PROJECTS USING ANONYMOUS PEER FEEDBACK
Bibliographic record
Abstract
Assessing the leadership abilities of engineering students for the purposes of accreditation and outcomes assessment is a particularly challenging task. For a few students, clear evidence of leadership will exist. These students naturally take on leadership roles within their teams. They have the complete confidence and support of their teammates. The team functions as a cohesive unit. In such a situation, an instructor can easily recognize the quality of leadership provided but may struggle with quantitatively assessing the leadership abilities exhibited. In other cases, students arguably possess leadership abilities yet fail to demonstrate their abilities in a way that can be assessed by an instructor. Such students might demonstrate leadership within their team by taking on an undesirable task or by taking on a disproportionate share of the workload. If a student perceives that leadership is not required, a student might demonstrate great leadership by not taking on an obvious leadership role. Peer feedback can help an instructor identify such examples of leadership.To enable proper assessment of leadership abilities,a suitable environment must be created such that all students have a natural opportunity to demonstrate their leadership skills in their own way. Such an environment is often created by a capstone project. Given the scope of a capstone project, all team members typically have several opportunities to take on a leadership role and/or demonstrate leadership.The challenge is to assess leadership quantitatively without forcing a change in the behaviour ofthe students. This paper examines the design and implementation of an anonymous peer feedback survey for the purpose of quantitatively assessing different measures of teamwork and leadership abilities in engineering students. This paper describes the challenges that are associated with such an assessment process. This paper also discusses the advantages and disadvantages of the proposed assessment technique.
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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 | 0.043 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".