Determining the Effectiveness of the 3D Alice Programming Environment at the Computer Science I Level
Bibliographic record
Abstract
Student retention in Computer Science is becoming a serious concern among Educators in many colleges and universities. Most institutions currently face a significant drop in enrollment in Computer Science. A number of different tools and strategies have emerged to address this problem (e.g., BlueJ, Karel Robot, etc.). Although these tools help to minimize attrition, they have not made significant improvements to this widespread problem. A newcomer to the scene called Alice has been met with positive results by captivating student interest through its rich 3D visual programming environment. During the fall of 2005, Alice, a newly published textbook, and numerous resources were used in Computer Science I at McMaster University. This article provides an overview of Alice, an assessment of this new course including qualitative surveys, informal observations, and quantitative analysis including student performance score results. Despite numerous technical problems, it was found that the Alice Group exceeded the performance of Comparison Groups: F(1, 93) = 30.322, p < .001 (between C1 and Alice group); F(1, 81) = 4.182, p = .044 (between C2 and Alice Group).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".