Effectiveness of Multi-Abstraction Computing Tools on Promoting Exploratory Self-learning in Engineering: a Case Study using a Custom Real-Time Operating System for Remote Learning
Bibliographic record
Abstract
This manuscript reports the results of an experiment to assess the effectiveness of multi-abstraction computing tools on promoting exploratory self-learning in engineering, using a custom Real-Time Operating System (RTOS) during remote learning. Our goals were to determine whether students respond to guided exploratory learning opportunities during remote learning, and whether these contributed to immediate and further student success. Students in a 3rd year course were given the custom multi-abstraction RTOS. The higher abstraction layer was taught directly, whilst students were directed towards exploratory self-learning among the lower abstraction layer (which includes details beyond prior knowledge or the content of the course). In a survey, students were asked about their perceptions of using higher and lower abstraction layers and how these impacted their learning. Responses were correlated with course grades. Students that engaged with the lower abstraction layer demonstrated higher success (3 percentage points above class average), engagement (22.5% reporting they were more engaged), accuracy of self-assessment (1.3 percentage points above class average), and learning beyond course's expectations (14% reporting they learned beyond the course's learning outcomes).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".