Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".