Notice bibliographique
Résumé
Baylor and collaborators have demonstrated (Baylor & Plant, 2005; Baylor et. al., 2006) that the use of virtual pedagogical coaches portrayed as young and attractive women can increase the willingness of female students to apply for technical education and to help increase their selfefficacy. Pedagogical processes such as role modelling and identification seem to be involved (cf. Bandura, 1977; Bandura et al., 1981). However, when analysing Baylor et al.’s results in detail, it appears that the increase in selfefficacy partly stems from a general conception of female engineers as less competent than male: “If she can do it, then I can do it”. This implies a potential conflict between a short-term pedagogical goal as to recruitment and boosted self-efficacy in students, and a long-term pedagogical goal regarding a desired change of gender prejudices and stereotypes. In an ongoing project (“Challenging Gender Stereotypes – using Virtual Pedagogical Agents”) we explore the possibilities to use androgynous virtual coaches for recruitment purposes, with a focus on students applying for educations with clear male or female dominance (and thus associated with gendered stereotypes). By June this year we expect to have results from an empirical study with about 100 participants, with qualitative as well as quantitative data. It is this study that we would like to present and discuss at the conference. Furthermore we wish to address and problematize broader issues on gender stereotypes and gender representation in the pedagogical use of digital media (and related issues regarding class, ethnicity, etc.). In our view, potentials, pitfalls as well as responsibilities accompany the increased degrees of freedom of representations in digital media, and we want to discuss how decisions regarding whose voice and whose appearance shall be exposed (in terms of gender, age, ethnicity, class, regional subgroup, etc.) can be made. References ”Challenging Gender Stereotypes – using Virtual Pedagogical Agents”; http://wwwold.eat.lth.se/Personal/Magnus/project_GLIT/HomePage_Eng.htm Bandura, A. (1977). Social learning theory, Prentice Hall. Bandura, A., & Schunk D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest throught proximal self-motivation. Journal of Personality and Social Psychology, 41(3), 586-598. Baylor, A. & Plant, E. (2005) Pedagogical agents as social models for engineering: The influence of appearance on female choice. Proceedings of AI-ED (Artificial Intelligence in Education), Amsterdam. Baylor, A., Rosenberg-Kima, R.., & Plant, E. (2006). Interface Agents as Social Models: The Impact of Appearance on Females’ Attitude Toward Engineering. CHI 2006 (Conference on Human Factors in Computing Systems). Montreal, Canada. Gulz, A. & Haake, M. (2006) Pedagogical agents – design guide lines regarding visual appearance and pedagogical roles. IV International Conference on Multimedia and ICT in Education (M-ICTE2006), Sevilla, 2006. Gulz, A., Ahlnèr, F., & Haake, M. (submitted) Visual femininity and masculinity in synthetic characters & patterns of affect (submitted). Haake, M. & Gulz, A. (2007) Virtual Pedagogical Agents: Stylisation for Engagement. Interfaces Magazine 70, Spring 2007 (in press). Haake, M. & Gulz, A. (submitted): Aesthetic stereotypes and virtual pedagogical agents.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».