Updating our Understanding of the Impact of Pre-College Computing Experiences on University Students
Bibliographic record
Abstract
This WIP Research paper is a follow-up to a study conducted in 2013 by McGill, Decker, and Settle that investigated the effects of pre-college computing experiences on students' decisions to study computer science at university. Their results indicated that the exposure to pre-college computing activities impacted students in different ways, particularly when looking at perceived impact by men and women participants [1], [2]. After six years and a myriad of changes to the K-12 computing landscape, it was time to see if impacts to current undergraduates were different than previously observed. This current study is a pilot qualitative study in which we interviewed seven undergraduate students about their experiences with computing prior to college. We were particularly interested in finding out about the nature of their computing experiences, whether they enjoyed them, and what they would change to make such experiences better for future participants. We used grounded theory and thematic coding to encode the interview transcripts, enabling us to look for common themes among the interview subjects. We are looking for elements in the interviews that could point to key differences in the pre-college computing landscape that have impacted student experiences that are different from those previously observed and thus impacting their experiences in university with computing. The goal of this preliminary study was to get a sense of what aspects of students' exposure to pre-college computing experiences have changed since 2013 and what changes should be made when creating a follow-up to this initial study.
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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".