Cards for Alternative Research Design (CARD): Refining and Evolving a Research Knowledge Development Activity for Computer Science Education
Bibliographic record
Abstract
One of the most important choices a researcher makes is selecting a research paradigm and methodology, without which they will be hampered in their search for knowledge and answers. Ideally, researchers consider all possible approaches and select the most appropriate one, but several factors constrain this: Time, familiarity with certain approaches, and the uncertainty of the benefit of change. \Cer draws from many research disciplines, exposing new possibilities that may not be seized due to these limitations. The Cards for Alternative Research Design (CARD) deck is designed to expand researchers awareness of different research approaches through a card-based prototyping exercise. This serious card-based game approach could be used by graduate students, early-career researchers, research course instructors, research mentors, and even experienced researchers. CARD games are intended to reduce the formality and potentially confrontational aspects of being asked to consider new approaches, allowing participants to examine their current research and plans through different paradigms, methodologies, and constraints, without it being a direct criticism of their current choices. This can increase the level of understanding of research framing and practice, strengthening the arguments for using a given approach and introducing valid arguments to adopt different approaches, with low time investment. This report summarizes the current evolution of the CARD deck, including an accompanying glossary and multiple games that can be played with the cards.
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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 | 0.283 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".