TEACHING FUNDAMENTAL COMPUTER PROGRAMMING CONCEPTS TO MECHANICAL ENGINEERING STUDENTS USING PALPABLE INTERACTIVE VISUAL LEARNING AIDS
Bibliographic record
Abstract
Pointers have long been the Achilles heel of mechanical engineering students attempting to master dynamic memory allocation in mechatronic applications. They are abstract and intangible, both opposing characteristics of a discipline based on the concrete (and often hands on) physical world. With this said, pointers are considered an important threshold concept opening the door to the implementation of complex microcontroller applications in our digitally connected world.One of the primary challenges in learning the application of pointers is that the programming syntax and the abstract memory management concepts are often taught simultaneously. The natural progression of learning is to first comprehend the concepts followed by the syntax. Further newer learning theories suggest a conceptual understanding can only result through abstraction of experiences using metaphorical linkages.The following research body is focused on proposing a new strategy for teaching this complex concept using low cost physical props as a palpable interactive visual medium to provide the requisite experiences for concept abstraction. The learning aids are designed to enforce a strict process flow mimicking the invisible actions occurring internal to the microprocessor. Data is collected via questionnaires administered pre and post lecture delivery. Analysis of the results suggest moderate to high improvement in student comprehension of computer memory allocation concepts
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".