Computational skills by stealth in introductory data science teaching
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
Abstract In 2010, Nolan and Temple Lang proposed “integration of computing concepts into statistics curricula at all levels.” The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the stealth development of computational skills in students' exposure to introductory data science through careful, scaffolded exposure to computation and its power. Our intent is to support students, regardless of interest and self‐efficacy in coding, in becoming data‐driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data‐driven inquiry. Reference is made to the computer science and statistics consensus curriculum frameworks the International Data Science in Schools Project (IDSSP) recently published for secondary school data science or introductory tertiary programs, designed to optimize data‐science accessibility.
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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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