Students of all ages and educational levels increasingly watch instructional videos for informal learning purposes online on websites such as YouTube and Google Videos, but such videos are also increasingly used in formal learning (Lenhart 2012; Spires et al. 2012). In formal learning, online instructional videos can be consulted while making homework, or can replace activities that normally took place face to face. For instance, some educators have even argued in favor of a “flipped classroom”, which entails having learners study videos at home to free up time in school for practice and teacher support (Bergmann and Sams 2012). Various types of videos are used for both informal and formal learning purposes, such as web lectures (e.g., Day and Foley 2006; Traphagan et al. 2010), short knowledge clips (e.g., Day 2008), and how-to demonstration videos (e.g., Ayres et al. 2009). Regarding the latter, research inspired by social-cognitive theories such as social learning theory (Bandura 1977, 1986) and cognitive apprenticeship (Collins et al. 1989) has demonstrated the effectiveness of acquiring problem-solving skills from these so-called video modeling examples in which a (human) model explains and/or demonstrates how to perform a task on video (e.g., Groenendijk et al. 2013a, b; Hoogerheide et al. 2014; Van Gog et al. 2014). In addition to being effective for acquiring cognitive skills, observing video modeling examples has also been shown to enhance affective variables, such as students’ belief in their own ability to perform the modeled task at a certain level (i.e., self-efficacy; Bandura 1997; Schunk 1987).

When creating a video modeling example, an instructional designer is confronted with various design choices, which might affect learning, both cognitively as well as affectively. For instance, should the video present a natural task performance procedure, which might entail making and correcting errors (e.g., Groenendijk et al. 2013a; b), or a more didactical procedure that reflects how a student should ideally learn the skill (e.g., Hoogerheide et al. 2014; Simon and Werner 1996; Van Gog et al. 2014)? Another design consideration is whether the model should be (partly) visible in the video while explaining the task (e.g., Hoogerheide et al. 2014; Van Gog et al. 2014; Xeroulis et al. 2007), or whether only the model’s computer screen should be shown (e.g., McLaren et al. 2008; Van Gog 2011; Van Gog et al. 2009). If a form is chosen in which the model is visible, the question arises who the model should be in terms of expertise, age, background, and gender.

Because the widespread use of online video modeling examples is relatively recent, there is as of yet little empirical knowledge available to guide design choices. Recent studies have started to uncover effects of different ways of presenting the content in video modeling examples (e.g., to which degree the model should be visible; Hoogerheide et al. 2014; Van Gog et al. 2014). Potential effects of model characteristics that are unrelated to how the learning task is presented, such as gender, on the learning process and learning outcomes, have received little attention in recent research on video modeling examples. However, earlier research inspired by the model-observer similarity hypothesis (Schunk 1987, 1991), as well as recent research on pedagogical agents (e.g., Baylor and Kim 2004; Ozogul et al. 2013), suggests that similarity in factors such as gender may matter. Building on these findings, which will be reviewed below, the present study examined whether the effectiveness and efficiency of video modeling examples can vary as a function of the observer’s and model’s gender.

Model-observer similarity

The model-observer similarity hypothesis (Schunk 1987, 1991; see also the similarity-attraction hypothesis; Moreno and Flowerday 2006) states that model characteristics can matter when learning from modeling examples because the effectiveness of modeling is at least partly moderated by the degree to which observers perceive a model to be similar to them. Modeling evokes social comparison (Berger 1977; Johnson and Lammers 2012) and observing a model that successfully performs a task may lead observers to believe that they can perform the task as well, if they identify with the model (Bandura 1981; Schunk 1984). Moreover, an observer may be more attracted to and pay more attention to a model that is perceived as similar (Berscheid and Walster 1969).

As Schunk (1987) noted, “similarity serves as an important source of information for gauging behavioural appropriateness, formulating outcome expectations, and assessing one’s self-efficacy for learning or performing tasks” (p. 149). It is likely that particularly novice learners whose prior knowledge as well as self-efficacy and perceived competence are still low, are affected by model-observer similarity, as they are especially likely to engage in social comparison (Buunk et al. 2003). In other words, the higher the degree of similarity between observer and model, particularly when the observer is novice to the task at hand, the more cognitive outcomes of learning (e.g., performing the same or novel tasks) and affective aspects of the learning process (e.g., self-efficacy, perceived competence) may be enhanced.

With respect to those affective variables, self-efficacy is important because it influences factors such as academic motivation, study behaviour, and learning outcomes (Bandura 1997; Bong and Skaalvik 2003; Schunk 2001). 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 1983, 1984).

Gender can perhaps be expected to be the most important factor of model-observer similarity because gender is among the first things being noticed when interacting with others (Contreras et al. 2013). Schunk (1987), however, reported mixed results on both learning outcomes and self-efficacy in his review, and suggested that one possible explanation for these mixed findings might lie in the appropriateness of the modelled behaviour: students’ beliefs that a skill or behaviour is more appropriate for one of the genders may moderate effects of gender similarity. This might explain why Bandura et al. (1963) and Hicks (1965) found that for boys, observing a male model displaying aggressive behaviour towards a doll led to more imitative aggression than observing a female model. In contrast, no such effects were found for grade 4–6 students who observed a male or female model solving fraction problems (Schunk et al. 1987). Although mathematical tasks are typically more associated with males than females (Forgasz et al. 2004; Stewart-Williams 2002), young children do not yet seem to hold this association, which becomes stronger during adolescence (Steffens et al. 2010; see also Ceci et al. 2014). In other words, the 10 year olds in the study by Schunk et al. (1987) may have been too young to associate a mathematical task with gender.

More recent studies also suggest mixed findings, however. 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. 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).

Several animated pedagogical agent studies, in which a cartoon-like (humanoid) agent functions as a model or teacher, did show a preference for male agents, particularly for tasks that may be believed to be more appropriate for men. For instance, Moreno (2002) found that university students’ knowledge about blood pressure was enhanced more after interacting with a male agent than a female agent. Arroyo et al. (2009) found that for secondary education and university students, a male agent led to more positive attitudes about mathematics and better learning outcomes. Furthermore, a study in educational technology found that male agents were evaluated as more interesting, intelligent, useful, and satisfactory than female agents (Baylor and Kim 2004). However, other research has shown that when learning an engineering task, often considered a stereotypically male domain in Western countries, interacting with a female model decreased women’s beliefs about engineering stereotypes compared to interacting with a male agent (Rosenberg-Kima et al. 2008). Moreover, when given the choice, students tend to select an agent of the same gender (Ozogul et al. 2013).

In sum, the model-observer similarity hypothesis suggests that if one observes a same-gender model, affective and cognitive aspects of learning are more enhanced. More recent studies, particularly those with animated pedagogical agents, seem to suggest however, that for tasks that are more appropriate for males, male agents are preferred over female ones. Therefore, when it comes to video modeling examples, it is still an open question how gender affects learning.

The present study

The present study investigated whether it is more effective for male and female secondary education students to study video modeling examples depicting a same-gender model explaining and demonstrating a math task in terms of cognitive aspects of learning (i.e., learning and near transfer) and motivational aspects of learning (i.e., self-efficacy and perceived competence). In addition, the study measured cognitive load (i.e., effort investment) during the learning and test phase to investigate effects on the learning process and explored effects on judgment of learning accuracy and instruction evaluation. Female and male secondary education students learned how to solve probability calculation problems with replacement and order important by watching a video modeling example in which either a male (see Fig. 1) or a female (see Fig. 2) model explained and demonstrated the task. Both were instructed to wear a neutral, black t-shirt, and participated in an extensive practice training session to ensure that they showed the same behaviour throughout the video (e.g., identical movements and gestures). An autocue was used to guarantee that the models gave the same explanation and spent the same amount of time on the steps shown in the video (and consequently on the video as a whole). After sufficient practice (as judged by the first author who was present at all times), the definitive recordings were created. Moreover, other factors that might affect perceived similarity were kept constant across conditions by selecting a young adult male and female Caucasian model (the majority of our participant population was Caucasian), who had a comparable educational background and were both in their early twenties. Therefore, we could be confident that effects (if any) would not be caused by differences in the content that was being presented.”

We firstly hypothesized that for male and female secondary education students who have little if any knowledge of solving probability calculation problems, it would be effective to study video modeling examples with both a male and female model, because research has consistently shown that example-based learning is very effective and efficient for novice learners (Atkinson et al. 2000; Renkl 2014; Sweller et al. 2011; Van Gog and Rummel 2010).Footnote 1 Thus, we expected high pretest to posttest performance gains (Hypothesis 1a) attained with a low to medium amount of effort investment during example study (Hypothesis 1b), while the amount of mental effort required to solve the test problems would decrease (Hypothesis 1c). Students’ self-efficacy and perceived competence were also expected to increase from pretest to posttest (Hypothesis 1d), since observing a model successfully explain and demonstrate a task has been shown to positively affect novices’ confidence in their own abilities (Bandura 1981; Hoogerheide et al. 2014; Schunk 1984).

The more interesting and open question was whether model-observer similarity would have an effect on cognitive and affective variables. In other words, would male and female students differ in the degree to which learning and transfer (Question 2a) and self-efficacy and perceived competence (Question 2d) would be enhanced, mental effort invested in the test reduced (Question 2c), and in the degree that students invest mental effort during example study (Question 2b), depending on whether they observed a video modeling example that presented a male or a female model? 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 1983, 1984), differences in perceived capabilities across conditions could affect how much mental effort students invest during example study.

Because enhanced confidence can also be a negative outcome if it leads to overconfidence, which can be detrimental to students’ regulation of their learning process (Dunlosky and Rawson 2012; Rhodes and Tauber 2011; Thiede et al. 2003), we instructed participants to predict their performance on the posttest. This judgment of learning was then matched to their actual performance to explore whether students’ judgment of learning accuracy would depend on the gender of the model (Question 3). Because an increase in confidence leads to using more cognitive and metacognitive strategies (Pajares 2006), differences might especially arise if students differ in their self-efficacy and perceived competence depending on the model’s gender.

Previous research has shown that instruction evaluation measures such as learning enjoyment may vary depending on the form of example-based instruction (Hoogerheide et al. 2014; see also Liew et al. 2013), and therefore we also explored effects on learning enjoyment and willingness to receive similar instruction in the future (Question 4) because these can be important indicators for the use of online examples during future self-study (Yi and Hwang 2003). Differences on these instruction evaluation measures might especially be dependent on whether there are differences in effort investment during example study because when practice effort decreases, enjoyment of practice may increase (Hyllegard and Bories 2009).