Retention in Computer Science Course
Bibliographic record
Abstract
Throughout its history, careers in fields related to Computer Science (CS) have expanded. However, there have been periods, like the year 2000 following the 'dot-com bust' when skilled people had to change employers, invoking negative publicity. The effect of this on enrollment in CS education was dramatic and lasted until 2007. According to Computing Research Association (CRA), there was an amazing increase in CS enrollment in 2008. Total enrollment per department by majors and pre-majors in U.S. CS programs is up 6.2% in 2008, and if only majors are considered, the increase is 8.1% CS student data are similar in Canadian schools There is a significant increase in number of enrollments in Computer Science education since 2008 The vital next step is to retain the enrolled students in Computer Science courses. Keeping that in mind, our research has combined the understanding of retention issues with some action strategies. This paper describes a first year CS course with this objective, initial finding and recommends some strategies to help students to successfully complete the course. In this research, we seek the clear indicators of withdrawal from or unsuccessful completion of a first year CS course. We suggest strategies to reduce the withdrawal rate and provide students with greater confidence in their ability to succeed. Our recommendations are for exam-ple, adapting the curriculum to the learners expectations i.e., making early assignments easier and less intimidating. Overall, the number of female students in computer science is low Our suggestion is designating support for female students through the course.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".