A STORY TELLING APPROACH FOR TEACHING ENGINEERING COURSES: A MINI-CASE METHOD
Bibliographic record
Abstract
Engineering courses should connect theory and practice, promote critical thinking, and engage students. Traditional teaching methods are becoming less appealing, and new strategies such as Problem-Based Learning and Flipped Classrooms have not been widely adopted. Case studies have been tried before, but open-ended ones require a complete overhaul of teaching methods and can end up feeling like projects rather than classroom tools. The mini-case approach is introduced as an in-class teaching tool to better engage students and show them the real-world implications of what they are learning. The mini-case structure includes a story, objective statement, student ideation, synthesis of students' ideas, simplification and working model, solution, conclusion, and discussion. The story is crucial to generating interest and retaining attention. The objective statement summarizes the case study's story, goals, and constraints in a single sentence. Students ideate about how to approach the problem, and their ideas are synthesized into a model that is simplified to fit the class's constraints. The solution is either analytical or numeric, and the case study objective statement is revisited to examine whether the requirements and criteria have been met. Reflection on the process and what the results mean is encouraged. The Mini Applied Case Studies (MACS) framework was developed as a structured approach to problem-solving in engineering courses. In the context of a Dynamics class, the framework was applied to the crank-slider mechanism, a challenging topic that students often struggle to understand. The MACS method successfully engaged students and developed their problem-solving skills. Feedback indicated that students found the method effective and interactive. Further research is needed to determine the full potential of the MACS method in other academic 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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".