Examining the adoption of big data and analytics curriculum
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
Purpose The purpose of this paper is to explore the demand for big data and analytics curriculum, provide an overview of the curriculum available from the SAP University Alliances program, examine the evolving usage of such curriculum, and suggest an academic research agenda for this topic. Design/methodology/approach In this work, the authors reviewed recent academic utilization of big data and analytics curriculum in a large faculty-driven university program by examining school hosting request logs over a four-year period. The authors analyze curricula usage to determine how changes in big data and analytics are being introduced to academia. Findings Results indicate that there is a substantial shift toward curriculum focusing on big data and analytics. Research limitations/implications Because this research only considered data from one proprietary software vendor, the scope of this project is limited and may not generalize to other university software support programs. Practical implications Faculty interested in creating or furthering their business process programs to include big data and analytics will find practical information, materials, suggestions, as well as a research and curriculum development agenda. Originality/value Faculty interested in creating or furthering their programs to include big data and analytics will find practical information, materials, suggestions, and a research and curricula agenda.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| 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