THE IMPACT OF STUDENTS’ ACADEMIC LOCUS OF CONTROL AND PERCEPTION OF PROBLEM SOLVING ABILITY ON THEIR PERFORMANCE IN DESIGN PROJECTS
Bibliographic record
Abstract
The University of Ottawa has introduced newintroductory engineering design courses that introducesstudents to engineering design through a CollaborativeProject Based Learning (CPBL) environment as it is knownfor enhancing deep learning, motivating students tocultivate interdependence in learning, problem-solving,and creating interest and excitement in learning. Studentswork in teams with a client to solve an engineering problemand develop and iterate prototypes. This paper aims atunderstanding the impact of those two hands-on courses(Engineering Design and Introduction to ProductDevelopment and Management) on engineering students’design skills as well as the factors that impacted thestudents learning. The factors that are considered in thisstudy are students’ prior knowledge or experience ofengineering design, students’ construct locus of control,perception of problem solving ability and team dynamics.A pre-& post-test was administered to students using avalidated design process skill assessment tool to quantifystudents’ progress during the course. Students’ finalprototypes were also assessed by external judges from thecommunity to evaluate the quality of students’ designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".