2006 Survey Results for CS1310: Introduction to Programming Using Media Computation
Bibliographic record
Abstract
In Spring of 2006, the Computer Science Department at the University of Texas at El Paso began an initiative to offer a course named CS 1310 Introduction to Computing, focused on media computation. The course was directed toward Liberal Arts or non-CS students with the main goal of exposing these students to fundamental concepts of computer science using a highly abstract language called Python. The course was structured similar to that of the CS 1315 Introduction to Media Computation course offered at Georgia Institute of Technology, designed by Associate Professor, Mark Guzdial.\nThe remainder of this document serves to evaluate the survey and coursework of CS 1310�s first two semesters. Each semester, two surveys were collected from the students; one during the first week and the other during the final week. These surveys as well as the corresponding class observations are the basis for the tables, diagrams, and written evaluations that follow.\nIt is the goal in publishing these results that we begin to assess both the academic and personal impact of offering a course of this type to students of various disciplines at UTEP. Exposing students of all disciplines supports the fact that computing and the need for computer adept professionals is a multi-disciplinary requisite. This need can be addressed by providing courses focused on the fundamentals of computing using techniques that promote interest, challenges and creativity for the students.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".