Benefits of Statics Concept Mapping in Career Cognition
Bibliographic record
Abstract
The purpose of this research was to develop a classroom project module that supported students in developing conceptual understanding of topics in statics, and building awareness of career value creation in engineering.The module developed includes a sequence of concept mapping activities that students complete that includes both technical topics and entrepreneurial mindset topics.The concept mapping activities were collected from students and scored using traditional and holistic approaches.The students completed a survey at the end of the concept mapping activities to provide insights about their experiences.The concept mapping for technical topics was found to be a useful formative assessment tool for students to connect concepts in the course.The value creation career results were compared with prior studies of engineering students who developed concept maps based on entrepreneurial mindset, and found to be very similar.The results indicate that this type of simple concept mapping activity can have benefits for students early in their engineering coursework to reflect on mindset and technical knowledge.think about long-term career connections, one of the key ideas of EM.Concept mapping has been used infrequently in Statics courses, but offers a useful formative assessment tool.This module is also part of a larger effort at the University of Washington Tacoma to expose students and faculty to the entrepreneurial mindset in engineering.One facet of the entrepreneurial mindset (EM) as defined by the Kern Entrepreneurial Engineering Network (KEEN) is creating value, the idea that engineers may create value to society, economic value, or other types of value [2].This idea aligns well with the ABET SEO 4: an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".