Influencing middle school girls to study computer science through educational computer games
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle