Board 208: Breaking Through the Obstacles: Strategies and Support Helping Students Succeed in Computer Science
Bibliographic record
Abstract
Abstract The field of computer science remains highly skewed toward White and Asian males at institutions of higher education and the workforce. The demographic characteristics of students in Computer Science (CS) nationwide are typically not representative of the general population. The overarching goal of this NSF project is to explore when and to which degrees these imbalances are greatest and how the imbalances may influence students' opportunities to enter and paths throughout CS undergraduate programs. This poster/paper will present a portion of our findings obtained during a pilot qualitative study related to strategies and support for overcoming obstacles through a variety of actions (policies, programs, pedagogy) towards student success. The pilot was run in three different institutions of higher education in California and is designed to dive into the students' lived experiences describing their pathways to and through the CS degree. We designed the pilot study to validate our study instrument, namely, to test our protocol and questions. The pilot was running until we reached saturation when we did not obtain any new data from the introduction of the new participants, resulting in a total of seven participants. The pilot study used a population of convenience: a limited population of students who are soon to be graduated or graduated. Three of the participants self-identified as women and four as men. We also explored whether focus groups or individual interviews provided the most effective means for elucidating meaningful data. We organized one focus group (all women) and four individual interviews (men). The focus group provided a comfortable environment and might have facilitated synergistic outcomes through participant interaction. Our findings illustrate lived experiences and brought several issues to light. Positive experiences included engaging pedagogy, prior CS experiences, a summer bridge program, a research experience, and a feeling of belonging. Negative experiences included dry pedagogy, competitive situations, cliques being formed, and challenging team dynamics. The collaborative work environment showed positive and negative aspects, pointing to the need for a well-defined collaboration policy. Collaboration and team dynamics influenced social engagement and a sense of belonging that has been known to significantly increase success, retention, and graduation rates. We noticed the differences in the level of preparedness and its influence on the students' journey. We also explored the influence of soft skills, outlook, scholarly attributes, and support on the perception of the journey through the program. Although our participants have reported that they did not perceive any overt sexism or racism, we present the findings correlated with gender and race/ethnicity. Our future work will include possible fine-tuning of the protocol to discuss demographics and reflect upon the situations where the students might feel minoritized. Additionally, the students in the future study will be purposefully selected to examine experiences at multiple stages of the major with different support and preparation for a CS major (SES and first-generation status), or the students who are at risk of dropping out or who have already dropped out as they may reveal reasons and circumstances for attrition.
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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 | 0.001 | 0.000 |
| 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 | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".