Education Statistics: Looking for Case-Study for Modeling
Bibliographic record
Abstract
The article deals with the problem of using modeling in social statistics courses. It allows the student-researcher to build one-dimensional and multidimensional models of the phenomena and processes that are being studied. Social Statistics course programs from foreign universities (University of Arkansas; Athabasca University; HSE University, Russia; McMaster University, Canada) are analyzed. The article provides an example using the education data set – Guardian UK universities ranking in Social Statistics course. Examples of research questions are given, data analysis for these questions is performed (correlation, hypothesis testing, discriminant analysis). During the research the discriminant model with group variable – modified Guardian score – and 9 predictors: course satisfaction, teaching quality, feedback, staff-student ratio, money spent on each student and other) was built. Lower student’s satisfaction with feedback was found to be significantly different from the satisfaction with teaching. The article notes the modeling and statistical analysis should be accompanied by a meaningful interpretation of the results. In this example, we discussed the essence of university ratings, the purpose of Guardian rating, the operationalization and measurement of such concepts as satisfaction with teaching, feedback; ways to use statistics in education, data sources etc. with students. Ways of using this education data in group and individual work of students are suggested.
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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".