A Causation-driven Approach to Engineering Education Using Data Analytics and Machine Learning Tools
Bibliographic record
Abstract
This work presents a causation-driven approach to engineering education using data analytics and machine learning tools as an alternative to teaching/learning approaches based on combining the physical description of the phenomenon and using first principle and/or correlations/heuristics. We aim to increase the percentage of student participation during lectures, recognize the importance of using data analytics to understand a phenomenon, introduce students to machine learning tools as complementary analysis methods, and encourage students to perform research and find “different ways” to assess and solve an engineering problem. We illustrated our approach with three examples in two different domains: lectures and supervision, including (i) understanding the physical significance of the Nusselt number using exploratory data analysis, (ii) studying the impact of the reaction temperature on conversion using Bayesian structural time-series model to evaluate the effect of an intervention, and (iii) performing a heterogeneous treatment effect to assess the potential causation of combined demographic and environmental variables on health outcomes using Causal Random Forest. To evaluate the effectiveness of our approach, we quantified the percentage of student participation, which increased by more than 20%, as well as a set of generated lessons learned that attest to deepening the acquired knowledge.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".