Work in Progress: Curriculum Revision and Classroom Environment Restructuring to Support Blended Project-Based Learning in First-Year General Engineering Laboratory Courses
Bibliographic record
Abstract
This work-in-progress report details the restructuring of a three-quarter first-year general engineering laboratory course sequence ending in a term-long cornerstone design project.Motivated by a taskforce implemented in 2015 to improve the first-year common curriculum, this development effort affects the first two quarters of this three-quarter, first-yearprogram laboratory course sequence.Faculty representatives from all engineering departments in the college were assembled to address three goals.The first goal was the establishment of a course structure emphasizing professional skills and engineering design.Second was the creation of a database of "mini-projects" to be integrated into the new course structure.The third goal was the establishment of a blended learning environment which uses web-based lectures and assessments in conjunction with hands-on, problem-based-learning laboratory activities.Three design-focused mini-projects were piloted during the fall and winter quarters of the 2016 -2017 academic year.A professional skills-focused "micro-project" ran for the first three weeks of the fall quarter, followed by seven weeks of a design-focused "mini-project".Pilot sections in the winter quarter began with a different seven-week mini-project followed by three weeks of another professional skills-focused micro-project.The first three mini-projects developed for this effort were titled: Robot Instruments, Heat Engine, and the Supercap Car Challenge.During the fall and winter quarters, students in the pilot sections were given self-efficacy surveys before and after their projects based on a Likert-type scale.These gauged their impressions of the projects, and self-evaluated their relevant knowledge and abilities before and after the projects.Early results presented in this paper indicate an improved level of student satisfaction with the new course structure and the pilot mini-projects.Table 1: First-year engineering laboratory course sequence areas of emphasis. Engineering Professional SkillsTechnical communication, organization and presentation.Ability to work in teams.Time management and planning.Professional skills for co-op (resume, interviews, etc.).Project management (manage tasks, budget, etc.).How to use research resources.How to critically evaluate information (found online, in books, articles, etc.).Ability to interact with a diverse audience.Understand societal factors impacting engineering (aesthetics, ethics, sustainability, manufacturability, etc.).The business cycle of engineering; role of entrepreneurship.Different engineering disciplines.Ability to define engineering project success and/or performance enhancement.Ability to adjust to different cultures and understand different global needs and constraints.Understand role of research in engineering; gain experience in research.Engineering Fundamentals Programming and logical thinking.Use of mathematics in the design process.Use of basic science in the design process.Integration of science into the design process.Ability to decompose problems into sub-problems.Ability to define tasks in a systematic manner.Understand limits of measurement, basic statistical analysis, error and uncertainty, and interpretation of data.Use various measurement and fabrication tools and technologies.Understand the design process.Use CAD, modeling, and/or visualization tools.Ability to do and to design experiments, gather data, analyze, report and present data.Understand relationships between inputs and outputs in systems.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".