Influencing middle school girls to study computer science through educational computer games
Bibliographic record
Abstract
The shortage of females in computer science has been studied before. Computer games have long been one way teenage boys find an interest in Computer Science, but most of those games are not appealing to teenage girls. This paper describes the ongoing collaborative research project which is experimenting with the design of educational computer games. Our research has the objective to influence middle school girls to pursue computer science in high school and college. The games are designed to change the image of computing among middle school girls, and to instill confidence by teaching real computer science concepts through puzzles. Gail Carmichael and her team of graduate students at Carleton University designed and created an educational computer game (Grams House) in 2010 with a helping others story. The prototype game focuses on two computer concept puzzles. In summer 2012, two undergraduates Jennifer Latham, and Nathaly Lozano, at Kean University designed and created a companion game (Grams Grocery Shop) with more teen appeal, and two more puzzles. In fall 2012 the Kean University research team piloted the game pair in an after school program at Roselle Park, a local middle school, using attitude surveys and concept quizzes to determine the impact of the games among the students. The pilot games were successful with the middle school students. After they had played the games, many of the girls said they could see themselves studying computer science, even though before the games, very few girls had included computer scientist as one of their two hoped for careers. Statistics gathered during the pilot indicate the need to continue this research, with more students in different demographics, and with more researcher collaboration, in order to design more adaptive games, to determine what specifically about the games influenced the girls the most, and to gain insight into how they were learning the computer concepts in the puzzles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".