A Study: Exploring the Feasibility of Developing a Computer Science Online Degree Program at Tuskegee University
Bibliographic record
Abstract
This paper investigates the feasibility of developing an online degree for a computer science and information technology degree programs. Our motivation is to increase access to quality education with the aim of stimulating growth, attracting new students, and retaining our current student body. A survey was conducted of CS/IT online degrees which are offered completely online. The survey includes various historically black colleges and universities (HBCUs) and majority universities throughout the United States to gain a broad perspective on the current offerings. This study provides comparisons of some majority universities against smaller universities, especially HBCUs in terms of online education. The pros and cons associated with online education are discussed to further understand if it could help us attain our goals. The resource and requirements (infrastructure, software, tools, and training) needed to develop a degree completely online are presented. A wide variety of tools, technologies software programs available in both private and public domain are enumerated and described. A detailed discussion is presented based on our departmental and curricular needs, including a phased implementation strategy for creating undergraduate and graduate degree programs online. A cost and benefit analysis is given to determine roughly, the overall effort of implementing online degrees within the CS Department. Lastly, some recommendations and conclusion based on our findings from the cost benefit analysis is presented. The goal of this work is to provide other institutions pondering the implementation of online courses/degree programs with a holistic understanding of this endeavor in terms of magnitude and cost.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".