A Case Study of Online, Project-Based Graduate Education for Working Professionals
Bibliographic record
Abstract
This paper presents a case study of a codified design approach used to develop and deliver a portfolio of project-based, online graduate programs tailored to working professionals at a private technological research university in the Northeastern United States. These programs adopt a learner- and career-centered model, emphasizing engaging, practical, and fulfilling educational experiences. While prior research has explored learner perceptions of individual instructional strategies within specific courses or domains, this study examines holistic perceptions across an integrated portfolio spanning multiple disciplines. The programs were designed using a confluence of evidence-based instructional design models. The study begins by outlining the foundational design framework and highlighting the value of real-world, project-based learning, supported by dedicated faculty mentorship and a learning management system with a consistent navigational structure. The paper then presents a mixed-method analysis of learner survey data collected over two years. Results show that over 96% of learners expressed positive sentiments about their courses, faculty mentors, and projects. To deepen the analysis, a textual study of over 35,000 words from three open-ended survey questions was conducted using natural language processing and meta-theme analysis. This analysis found that supportive faculty mentorship, project-based coursework, and real-world application were key strengths of the learner experience. Areas for improvement centered on refining course pacing, ensuring clear alignment between materials and deliverables, and providing more timely feedback on project milestones. Ultimately, the results underscore the need for careful and intentional design of online programs with consideration for the learner's voice.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".