EFFECTIVENESS OF STUDENT LEARNING IN AN AEROSPACE ENGINEERING CAPSTONE PROJECT: INVESTIGATION OF ASSESSMENT METHODS
Bibliographic record
Abstract
A deep learning approach is focused on understanding concepts, analyzing ideas, and creating a strong connection between them and prior knowledge to solve real problems. A capstone project, considered as deep learning approach, is a widely-adopted educational strategy designed to teach students to use their engineering knowledge to solve real-life engineering problems. An aerospace capstone project has been introduced at Concordia University through the NSERC Chair in Aerospace Design Engineering (NCADE) program. The goal of this paper is to investigate assessment methods to measure the effectiveness of students’ learning considering cognitive (knowledge and skills) and affective domains of learning during the NCADE capstone project. To achieve the goal, four assessment methods (i.e., Study Process Questionnaire (SPQ), Approaches to Study Inventory (ASI), Concept Mapping Technique (CMT) and Recursive Object Model (ROM)) have been investigated. The paper also discusses the methods with the purpose of implementing an ongoing continuous improvement process during the capstone project.
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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 | 0.029 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".