Building Engineering Professional and Teamwork Skills: A Workshop on Giving and Receiving Feedback
Bibliographic record
Abstract
Abstract Proficiency in teamwork is one of the most essential skills employers seek in their new hires. As such, academic institutions are continually increasing their focus on teamwork education and assessment. This paper describes one module out of a series of six teamwork modules targeted at undergraduate engineering students. The series of modules have been designed to provide teamwork theory and skills in the context of an existing team project within a course, allowing the new knowledge and skills to be applied authentically and at the time of learning. Additionally, a number of assessment strategies have been developed in order to assess student learning and the overall success of the modules. The fourth module developed as part of this series on teamwork focuses on giving and receiving feedback. At the end of this module, students should be able to: 1. Understand the value of seeking, giving, and receiving feedback for themselves, their team, and as a professional; 2. Apply communication skills that keep feedback from becoming personal, both as a giver and as a receiver of feedback; and, 3. Give feedback that integrates various types of functions, including to understand, assess, and provide recommendations. Taking the format of a workshop, the module was piloted in five courses across different engineering disciplines, undergraduate levels, and courses. In an implementation in a first-year civil, environmental and geological engineering course, student teams exchanged feedback on preliminary designs completed prior to the start of the workshop. In addition, two classes of fourth-year management engineering students completed the module in their capstone design course, practicing giving and receiving feedback to their peers on conceptual designs as well as on design verification and validation plans. Finally, the workshop was also run in a second-year systems design engineering class as a standalone unit (not connected to a course project). In that offering, students developed different design concepts to address a design need suggested by the instructor and subsequently exchanged feedback on the designs with their peers. In all cases, students were able to reflect on their learning experience by answering survey questions and/or reflecting on the value of this module in their design reports. In general, students responded very positively to this learning experience, with each round of student feedback providing greater insight into how the workshop could be modified to ensure that students recognize the value of the feedback process as a professional skill and that they become comfortable giving and receiving feedback to each other. We describe the contents of the workshop in detail and summarize student feedback on each implementation. Further, we reflect on how the workshop can be further developed to better meet its intended learning outcomes and suggest ways in which instructors can alter it to suit different student disciplines, academic levels and course objectives.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".