Bibliographic record
Abstract
The capstone project is a culminating experience whereby students choose to research a topic that is relevant in their field of study and have been highly regarded as important learning activities. The capstone allows students to use research, analytical, problem solving and evaluation skills they have learned in the course of the graduate program. McGill indicates the benefit of the completion of a capstone project not only in gaining knowledge in capstone but also in learning how to apply the knowledge gained in other courses in the major (McGill, 2012). Currently, COPACE (College of Professional and Continuing Education) has three separate capstone programs – one each for MPA, MSPC, and MSIT. The goal of this study is to evaluate our existing capstones at COPACE and identify the components of a quality capstone program to benefit future capstones. This study will also determine whether integrating these three separate capstones programs into a single standard capstone is more beneficial than the current model. We will also show how to improve the existing capstones programs. The methods used in this study are: survey, broad research on other universities and analysis of 44 COPACE capstones. The target populations of the survey are COPACE graduate students graduated from 2013 to 2014. An anonymous questionnaire was sent by email to approximately 120 Clark students. The survey results are interpreted with suggestions for achieving more consistency in capstones.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.143 | 0.042 |
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".