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Ma, Xin; Kishor, Nand

doi:10.1023/a:1024785812050

Cited by 110 publications

(25 citation statements)

References 85 publications

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“…Self-concepts in turn, are consequently to the social-cognitive theory, strongly related to an individual's achievement (Denissen, Zarrett, & Eccles, 2007). A wide range of studies have not only corroborated the BFLPE, but also found this positive relation of self-concept with achievement in mathematics and science (e.g., Antunes & Fontaine, 2013;Cvencek, Kapur, & Meltzoff, 2015;Jansen, Schroeders, & Lüdtke, 2014;Lee & Kung, 2018;Ma & Kishor, 1997;Sahranavard, Hassan, Elias, & Abdullah, 2012;Wang, Oliver, & Staver, 2008). Guided by this theoretical framework, Höffler et al (2017) and Chang and Lin (2017) found that also within a science Olympiad reference framework, participants' academic self-concept was significantly correlated with their academic achievement.…”

Section: Theories Used To Explain Participation and Achievement In mentioning

confidence: 88%

Gender differences in mathematics and science competitions: A systematic review

et al. 2019

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Gendered patterns in mathematics and science interest emerge in early childhood, develop over time, and ultimately reflect advanced course selection in secondary education. During the crucial time adolescents become aware of their strengths and interests and specialize accordingly, they get the opportunity to participate in out‐of‐school learning programs such as mathematics and science competitions. This raises the question whether mathematics and science competitions contribute to gender equity by equally promoting female and male interests. In this article, we present a systematic review on gender differences and the mechanisms explaining success and failure in mathematics and science competitions. On an international level, we found large gender differences regarding participation in all Olympiads with the exception of the biology Olympiad. In fairs and national Olympiads, overall participation rates were not gendered as such, but females preferred biology topics whereas males preferred physics related topics. Male and female achievement in fairs was comparable, but males clearly outperformed female participants at the Olympiads, with the smallest differences in the biology Olympiad. Variables and theoretical frameworks explaining participation and achievement and the role of gender in mathematics and science competitions are discussed. We suggest that gender stereotypes, through their influence on self‐concept and interest, play an important role in the mechanisms resulting in low female participation rates in and beyond mathematics and science competitions (especially in physics and chemistry). The mechanisms we found explaining female representation during a national selection competition might be considered as reflecting those in female mathematics or science careers and could thus serve as food for thought on countering the gender gap in mathematics and science.

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Section: Theories Used To Explain Participation and Achievement In mentioning

confidence: 88%

Gender differences in mathematics and science competitions: A systematic review

et al. 2019

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“…The affective variables of self-efficacy and perceived competence, both of which have been associated with better learning outcomes (Bandura 1997;Bong and Skaalvik 2003;Harter 1990;Ma and Kishor 1997;Schunk 2001), were also enhanced from pretest to posttest (Hypothesis 1d), although no effect of model-observer similarity was found (Question 2d). Male students did show higher self-efficacy than female students, which was, however, not associated with higher learning outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, perceived competence, which is a related construct that reflects broader perceptions and knowledge (Bong and Skaalvik 2003;Hughes et al 2011;Klassen and Usher 2010), also affects academic motivation and learning outcomes (Bong and Skaalvik 2003;Harter 1990;Ma and Kishor 1997). Moreover, when students' confidence in their own capabilities increases, they tend to use more cognitive and metacognitive strategies irrespective of previous achievement or ability (Pajares 2006) and the willingness to invest mental effort in a task changes as well (Bandura 1977;Salomon 1983Salomon , 1984.…”

Section: Model-observer Similaritymentioning

confidence: 99%

Learning from video modeling examples: does gender matter?

2015

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Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer similarity hypothesis suggests that such characteristics may affect learning. Therefore, this study investigated whether the effectiveness of learning how to solve a probability calculation problem from video modeling examples would vary as a function of the model's and observer's gender. In a 2 (Model: Female/Male) 9 2 (Observer: Female/Male) between-subject design, 167 secondary education students learned how to solve probability calculation problems by observing video modeling examples. Results showed no effects of Model or Observer gender on learning and near transfer. Male students reported higher self-efficacy than female students. Compared to a female model, observing a male model enhanced perceived competence more from pretest to posttest, irrespective of observers' gender. Furthermore, learning from a male model was less effortful and more enjoyable for male students than for female students. These results suggest that gender of both model and observer can matter in terms of affective variables experienced during learning, and that instructional designers may want to consider this when creating (online) learning environments with video modeling examples.

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“…The reference sections for all relevant studies and previous review articles (Haney & Durlak, 1998;Hansford & Hattie, 1982;Ma & Kishor, 1997;O'Mara, Marsh, Craven, & Debus, 2006;Valentine, 2001;Valentine et al, 2004) were subsequently examined for additional studies that were not identified in the data-based searches. These searches resulted in the identification of 1043 studies.…”

Section: Study Identification and Inclusionmentioning

confidence: 99%