Starting from Scratch: A Holistic Framework for Designing Digitally Delivered Graduate Programs for STEM Working Professionals
Bibliographic record
Abstract
Provided the opportunity to create new, high-quality graduate programs from scratch, a framework was sought to help meet the intersecting needs of employers and employees, while cultivating a learning environment that honors the individuality of working professionals. Given Rensselaer Polytechnic Institute’s (RPI) nearly 200 years of engineering and research excellence, it was natural to leverage the engineering design process to develop a modern solution. Though there are many variations of the engineering design process, this paper presents it as a sequence of six steps: identify, explore, design, create, test, and improve. The first three steps (identify, explore, and design) are the focus of this work. From these emerged a series of strategic decisions in program logistics (face-to-face, online, hybrid, etc.), curriculum design, and learning interface informed by thorough consideration of employer and employee needs as well as the latest in learning science. Due to the abundance of variety in how graduate programs are designed, this paper provides a detailed description of the design process such that other institutions looking to develop quality, digitally delivered programs, may consider this work. Consequently, clear connections are made between the needs of employers and employees, learning science, and research design. In all, eight graduate certificates, 10 sets of learning goals and competencies (LG&Cs), 25 project-based courses, and more than 60 projects have successfully been designed, developed, and delivered using the described process as a framework for program development. Future papers will explore how these courses and projects were created, tested, and improved through course development and review and revision processes that incorporate regularly cadenced instructor and student feedback.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".