Gender differences in the use and benefit of advanced learning technologies for mathematics.

Abstract

We provide evidence of persistent gender effects for students using advanced adaptive technology whue leaming mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style of use of the system, motivational goals, affective needs, and cognitive/affective benefits, as well as the impact of affective interventions involving pedagogical agents. We describe 4 studies, with hundreds of students in public schools over several years, which suggest that technology responses should probably be customized to each gender. This article shows differential results before, during, and after the use of adaptive tutoring software, indicating that digital tutoring systems can be an important supplement to mathematics classrooms but that male and female students should be addressed differently. Female students were more receptive than male students to seeking and accepting help provided by the tutoring system and to spending time seeing the hints; thus, they had a consistent general trend to benefit more from it, especially when affective leaming companions were present. In addition, female students expressed positively valenced emotions most often and exhibited more productive behaviors when exposed to female characters; these affective pedagogical agents encouraged effort and perseverance. This was not the case for male students, who had more positive outcomes when no leaming companion was present and their worst affective and cognitive outcomes when the female character was present.Keywords: adaptive leaming environments, gender differences, affective agents, motivation and affect, quantitative analysis Advanced learning technologies have the potential to personalize instruction for students and to meet individual learning needs. To provide such personalized instruction, researchers must first assess factors that influence student learning. A key factor in the domain of mathematics is student gender. To date, much of the educational psychology research on motivation and achievement has been conducted in standard classrooms without technology support. This article provides an analysis of the impact of gender in mathematics learning with instructional software, called Wayang Outpost. Gender differences were investigated by focusing on cognitive and affective factors in leaming. Findings from this research form a theoretical foundation for the design of adaptive computational tutors that deliver personalized support.

Comparing virtual and location-based augmented reality mobile learning: emotions and learning outcomes

“…In Study 1 two postdoctoral fellows, both men in their late twenties, served as the guide, whereas two female undergraduate research assistants in their early twenties served as the guides in Study 2. It is therefore possible that the significant difference in enjoyment attributed to the guide came from learners interacting with a guide of the same gender and closer to their own age (in most learners cases), as other studies have found with virtual pedagogical agents (artificial intelligence-driven avatars in tutoring systems; Arroyo et al 2013). A second limitation was that the battery for the guide's phone (using the MetaGuide app) died during the sessions for two of the outdoor participants.…”

The Role of Agent Age and Gender for Middle-Grade Girls

“…Many girls seem to be better motivated when a learning environment supports interpersonal connections and collaborative interactions than when these interactive features are missing (Belenky, Clinchy, Golberger, & Tarule, 1997). Similarly, educational technology research over the last decade has attested to gender difference in the use of interactive learning technologies: that is, girls are more likely than boys to benefit from frequent feedback and interactive help embedded in the tutoring system (Arroyo, Burleson, Tai, Muldner, & Woolf, 2013). Furthermore, girls increased their self-efficacy beliefs in learning mathematics after working with an animated agent embedded in computer-based learning (Kim & Lim, 2013).…”

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

“…These types of messages direct learners' attention to a pedagogical agent who is portrayed as also finding an activity boring or frustrating (Arroyo et al 2013(Arroyo et al , 2014McQuiggan and Lester 2007). The underlying mechanism here is an appeal to the learner to feel a certain way based on another's emotions, or in this case, the emotions of a virtual peer or tutor.…”

“…In general, despite strong evidence that direct, system-delivered prompts, reappraisal messages in particular, work in experimental learning and external learning contexts (Chauncey-Strain and D'Mello 2015; Hall et al 2006b;Leroy et al 2012), their effectiveness when deployed in ALTs seems to be mediated by individual differences such as prior knowledge and gender (Arroyo et al 2013;D'Mello et al 2010), and has occasionally been found to elicit negative emotions (Robison et al 2009). Moreover, ALTs often mix different types of system-direct prompts into system architectures under different names such as reactive empathy, which can make empirically evaluating their effectiveness more challenging (McQuiggan and Lester 2007).…”

“…Researchers have experimented with a number of approaches to help students regulate their emotions while interacting with ALTs such as empathetic messages (McQuiggan and Lester 2009), attention-eliciting prompts (D'Mello et al 2012), feedback emphasizing the malleability (e.g., control) of intelligence through hard work and persistence (Arroyo et al 2013), and the use of narrative to foster affective engagement (Rowe et al 2011). Researchers have also addressed the importance of emotion regulation in ALTs, describing them as proactive or reactive systems (D'Mello and Graesser 2015) building off of Shute's (1993) distinction between macro and micro adaptation in an emotion-specific direction.…”