Notice bibliographique
Résumé
Computing plays a significant role in our communities from the local to the global. Computing greatly benefits social causes such as community organization, education, humanitarian relief and information access. However, many students proceed through an undergraduate degree learning of the impact that computing has in business and science, but are not made aware of the positive social impact that computing has and will continue to have. Students are taught about the impact of computing for social good as part of the author's senior Software Engineering course at MacEwan University in Edmonton, AB, Canada. In particular, the humanitarian open-source software project called Ushahidi [1] (http://www.ushahidi.com) as the platform for group projects since 2011. Ushahidi is a crisis-mapping software project that was initially developed during the 2008 Kenyan elections to allow citizens to communicate issues (e.g., violence, intimidation, and voting irregularities) that the state media was not reporting. Ushahidi has continued as an actively developed open-source project and currently has thousands of deployments worldwide including over 3000 in the U.S. alone. It has been used to help communities during numerous crises such as the Haitian earthquake, Snowmaggedon and in Syria and other "Arab Spring" countries to report violence. Last year the IPython notebook (http://www.ipython.org) as a second option for my students. Categorizing IPython as a social good project is debatable, it has broadened the experience beyond a single open-source application. Using these projects to teach about computing's social impact over the last three years is of interest for three reasons: • Open-source - Use of popular open-source humanitarian software is a compelling option for student projects so that the students' work can have a broader impact and to expose students to real-world development communities. Open-source communities also vary in their tools and collaboration style, experiences in working with the IPython and Ushahidi communities are shared. • Breadth of projects -- Several example student projects and the feedback students have given as to what they enjoyed most and least about their projects are discussed. • Effective Practices -- Use of a common virtual machine to scaffold the projects so students can get software installed and running quickly, within an hour or two, and avoid configuration problems. In each year of the last three years the author tried different approaches in having the students work on projects. He also used different pedagogical approaches, incorporating both individual and group projects, allowed students to select their own projects versus assigning them and tried different project scopes, from very specific tasks (i.e., fixing bugs) to broader, open problems such as adding new features. Finally, he mentored the students himself as well as worked with external mentors. In 2013, the author presented a poster titled "Teaching Software Engineering with a Humanitarian Open-Source Software Project" at SIGCSE. Following that poster he collaborated with the Foss2Serve project (http://foss2serve.org) and mentored faculty members at other universities in incorporating humanitarian FOSS projects in their curriculum.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,620 | 0,384 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».