Unleashing the Power of Data Analytics to Examine Engineering Students’ Experiences and Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this theory paper, we integrate literature from different fields. We argue that efforts to expand engineering education research through data analytics need to be grounded in the established literature and understanding of student development. We discuss the opportunities and challenges associated with using data analytics to examine engineering students' experiences and outcomes. We suggest that engineering schools should enhance data infrastructure, along with data governance policies, to foster a culture of collaboration among units and divisions, and better utilize existing student data sources through greater data integration. We also suggest that engineering education researchers equip themselves with knowledge on data science, in addition to knowledge about different types of student experiences, and actively explore a wider range of data sources for research. Thereby, we envision a new research landscape with expanded data sources, integrated data systems, and new analytical techniques to enable predictive analysis and more actionable findings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it