Panel Session: Case Study Teaching in Computing Curricula
Bibliographic record
Abstract
Abstract Panel Session –Case Study Teaching in Computing CurriculaMassood Towhidnejad, Salamah Salamah, Thomas HilburnEmbry-Riddle Aeronautical University600 S. Clyde Morris Blvd.Daytona Beach, Fl, 32114towhid@erau.edu, salamahs@erau.edu. hilburn@erau.eduAbstractThe use of case studies is an effective method for introducing real-world professional practices into theclassroom. Case studies have become a proven and pervasive method of teaching about professionalpractice in such fields as business, law, and medicine. Case studies can provide a means to simulatepractice, raise the level of critical thinking skills, enhance listening/cooperative learning skills, anddevelop problem solving skills. Although the use of case studies in education has shown success in theabove mentioned disciplines, it is yet to be adopted in any significant way in the computing education.This panel session will explore central issues about the use of case study teaching: What is it? What areits advantages and challenges? Where and how should case study teaching be used? What resources andtraining support is available? In addition, to the panel discussion and Q &A, the session will engage theaudience in a simple exercise related to a smart house case study (http://www.softwarecasestudy.org/).Potential PanelistsDr. Sushil Acharya, Robert Morris UniversityDr. Steven Roach, University of Texas, El PasoDr. Salamah Salamah, Embry-Riddle Aeronautical UniversityDr. Walter Schilling, Milwaukee School of EngineeringDr. Massood Towhidnejad, Embry-Riddle Aeronautical University, Panel ModeratorReferences1. Thomas B. Hilburn, Massood Towhidnejad, A Case for Software Engineering, Proceedings of the 20th Conference on Software Engineering Education and Training (CSEET), Dublin, Ireland, July, 2007, 107-114.2. Salamah Salamah, Massood Towhidnejad, Thomas B. Hilburn, Reporting on the Use of a Software Development Case Study in Computing Curricula, Proceedings of 2011 ASEE Annual Conference & Exposition, Vancouver, BC, June 2011.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".