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Full-Text Articles in Engineering
Gendered Socialization With An Embodied Agent: Creating A Social And Affable Mathematics Learning Environment For Middle-Grade Females, Yanghee Kim, J. Lim
Gendered Socialization With An Embodied Agent: Creating A Social And Affable Mathematics Learning Environment For Middle-Grade Females, Yanghee Kim, J. Lim
Yanghee Kim
This study examined whether or not embodied-agent-based learning would help middle-grade females have more positive mathematics learning experiences. The study used an explanatory mixed-methods research design. First, a classroom-based experiment was conducted with one hundred and twenty 9th-graders learning introductory algebra (53% male and 47% female; 51% Caucasian and 49% Latino). The results revealed that learner gender was a significant factor in the learners’ evaluations of their agent (η2 = .07), the learners’ task-specific attitudes (η2 = .05), and their task-specific self-efficacy (η2 = .06). In-depth interviews were then conducted with 22 students selected from the experiment participants. The interviews …
The Impact Of User Attributes And User Choice In An Agent-Based Environment, Yanghee Kim, Quan Wei
The Impact Of User Attributes And User Choice In An Agent-Based Environment, Yanghee Kim, Quan Wei
Yanghee Kim
This study examined the impact of learners’ attributes (gender and ethnicity) on their choice of a pedagogical agent and the impact of the attributes and choice on their perceptions of agent affability, task-specific attitudes, task-specific self-efficacy, and learning gains. Participants were 210 high-school male and female, Caucasian and Hispanic students who worked at computer-based algebra integrated with pedagogical agents. The results indicated, first, that students preferentially chose a same-gender agent and a same-ethnicity agent, supporting similarity-attraction theory. Second, males who chose an agent showed more positive attitudes toward working at the learning environment than did males who were assigned to …
Understanding Student Pathways In Context-Rich Problems, Pavlo Antonenko, John Jackman, Piyamart Kumsaikaew, Rahul Marathe, Dale Niederhauser, Craig Ogilvie, Sarah Ryan
Understanding Student Pathways In Context-Rich Problems, Pavlo Antonenko, John Jackman, Piyamart Kumsaikaew, Rahul Marathe, Dale Niederhauser, Craig Ogilvie, Sarah Ryan
Sarah M. Ryan
In this paper we investigate the ways that students' problem-solving behaviors evolve when solving multi-faceted, context-rich problems within a structured, computer-based learning environment. During the semester, groups of two or three students worked on several problems that required drawing on more than one concept and, hence, could not be readily solved with simple "plug-and-chug" strategies. The problems were presented to students in a data-rich, online problem-solving environment that tracked which information items were selected by students as they attempted to solve the problem. The students also completed a variety of tasks, like entering an initial qualitative analysis into an online …