Causality Exploration in Modeling Engineering Student Satisfaction
Bibliographic record
Abstract
The goal of this paper is to develop a self-aware computational model aimed at analyzing student satisfaction in an engineering faculty. We examine whether student diversity, as well as student engagement, social events, and academic experience have a direct effect on student satisfaction and, consequently, retention and loyalty. A Confirmatory Factor Analysis to explore the associations between latent and observed variables led to the design of the Structural Equation Model. In our model, the following latent variables were positively associated with Student Satisfaction: Student Engagement, Academic Experience, Student Diversity, and Social Events. Also, the association between Student Satisfaction and Student Loyalty was positive. Other latent variables were tested such as Student Well-being and Student Support, but they did not provide a good result. Note that, unlike other papers in the area, we considered student diversity to make the model diversity-aware. The learned model provided the basis for the reasoning process behind engineering student satisfaction. In future work, this Structural Equation Model will become the basis for building the Bayesian network, the model that allows us to perform probabilistic inference and test various scenarios using the reasoning mechanism.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".