Children?s Responses to Computer-Synthesized Speech in Educational Media: Gender Consistency and Gender Similarity Effects

Abstract

This study examines children's social responses to gender cues in synthesized speech in a computer-based instruction setting. Eighty 5th-grade elementary school children were randomly assigned to one of the conditions in a full-factorial 2 (participant gender) 3 2 (voice gender) 3 2 (content gender) experiment. Results show that children apply gender-based social rules to synthesized speech. More specifically, children evaluate synthesized speech more positively, trust the speech more, and learn more effectively when voice gender matches either content gender (consistency attraction) and/or their own gender (similarity attraction). Children's computer self-efficacy was a significant covariate for their social responses to synthesized speech. Theoretical and practical implications of the current study for the design of educational media are discussed.

Learning from video modeling examples: does gender matter?

“…Surprisingly in light of the above, a study with university students learning probability calculation with dynamic visualizations accompanied by a male or female model's narration showed that a female model was preferred and led to better learning outcomes than a male model (Linek et al 2010). However, findings of Rodicio (2012) and Lee et al (2007) suggest the opposite, namely that male narrations should be preferred. More specifically, Rodicio (2012) found that university students learned more about geology from dynamic visualizations with a male voice-over than a female voice-over, and Lee et al (2007) found that for male students, a male computer-generated voice was more positively evaluated, trusted, and led to higher confidence levels than a female computer-generated voice.…”

“…However, findings of Rodicio (2012) and Lee et al (2007) suggest the opposite, namely that male narrations should be preferred. More specifically, Rodicio (2012) found that university students learned more about geology from dynamic visualizations with a male voice-over than a female voice-over, and Lee et al (2007) found that for male students, a male computer-generated voice was more positively evaluated, trusted, and led to higher confidence levels than a female computer-generated voice. Note though, that in these studies, the model was not visible and therefore the cues available to make a social comparison may have been less strong compared to video modeling examples with a visible model (Hoogerheide et al 2014).…”

“…Based on the model-observer similarity hypothesis, we could expect novice learners to identify more with a same-gender model relative to an opposite-gender one and therefore show cognitive and affective benefits when learning from a same-gender model (Schunk 1987). However, based on research with animated pedagogical agents (e.g., Arroyo et al 2009;Moreno et al 2002) and dynamic visualizations with a voice-over (Lee et al 2007; Rodicio 2012), we might expect that novices benefit more from a male model than a female model because mathematical tasks are associated more with males than females (Forgasz et al 2004;Stewart-Williams 2002). Moreover, because the confidence that learners have in their own capabilities is associated with how much effort they invest (Bandura 1977;Salomon 1983Salomon , 1984, differences in perceived capabilities across conditions could affect how much mental effort students invest during example study.…”

Interactive Learning Environments

“…The similarity attraction hypothesis in the context of learning with animated pedagogical agents would predict increased learning and more positive perceptions the greater the similarity between the learner and the agent. Previous research has explored agent similarity effects with regard to agent gender (Baylor & Kim, 2003;Behrend & Thompson, 2011;Lee, Liao, & Ryu, 2007;Moreno & Flowerday, 2006;Plant et al, 2009;Rosenberg-Kima, Plant, Doerr, & Baylor, 2010;Van der Meij, Van der Meij, & Harmsen, 2012), age (Rosenberg-Kima, Baylor, Plant, & Doerr, 2008) ethnicity (Baylor & Kim, 2003;Behrend & Thompson, 2011;Moreno & Flowerday, 2006;Pratt, Hauser, Ugray, & Patterson, 2007;Rosenberg-Kima et al, 2010), personality (Isbister & Nass, 2000;Moon & Nass, 1998;Nass & Lee, 2001), physical appearance (Rosenberg-Kima et al, 2008;van Vugt et al, 2010), and feedback style (Behrend & Thompson, 2011). Moreno and Flowerday (2006) randomly assigned learners to a choice condition, in which learners selected an agent from 10 options, differing in gender and ethnicity, or a non-choice condition, in which learners were assigned to an agent.…”

“…Results supported this hypothesis; the two conditions (male and female) with young and 'cool' agents led to higher self-efficacy and interest than the remaining six conditions. Lee et al (2007) explored gender similarity using a computerized voice only. The authors showed that male participants rated a male agent's voice more likeable than a female agent, whereas no difference in voice likeability was found for female participants.…”