Reasoning Mind Genie 2: An Intelligent Tutoring System as a Vehicle for International Transfer of Instructional Methods in Mathematics

Abstract

Effective mathematics teachers have a large body of professional knowledge, which is largely undocumented and shared by teachers working in a given country's education system. The volume and cultural nature of this knowledge make it particularly challenging to share curricula and instructional methods between countries. Thus, approaches based on knowledge engineering-designing a software system by interviewing human experts to extract their knowledge and heuristics-are particularly promising for cross-cultural curriculum implementations. Reasoning Mind's Genie 2 system demonstrates the viability of such an approach, bringing elements of Russian mathematics education (known for its effectiveness) to the United States. Genie 2 has been adopted on a large scale, with around 67,000 United States students participating in the 2012-2013 school year. Previously published work (some of it in peer reviewed articles and some in non-peer-reviewed independent evaluations) has associated Genie 2 with high student and teacher acceptance, increases in test scores relative to "business as usual" conditions, high time on task, and a positive affective profile. Here, we describe for the first time the design, function, and use of the Genie 2 system. Based on this work, we suggest general principles-which collectively represent a proposed methodology-for the design of intelligent tutoring systems intended for cross-cultural transfer of curriculum and instructional methods.

Incorporating effective e-learning principles to improve student engagement in middle-school mathematics

“…Reasoning Mind developed the Genie 2 platform in 2005, designing instruction in line with the practices of expert teachers (Khachatryan et al 2014) within a system where students receive immediate, individualized feedback while learning, primarily from a pedagogical agent known as the Genie. However, the design of this platform did not purposefully incorporate research-based principles of e-learning such as those in Clark and Mayer (2011).…”

“…Developed by the nonprofit company of the same name, Reasoning Mind currently provides blended learning instruction in mathematics to over 100,000 students in the United States. Reasoning Mind works with expert teachers to design online learning experiences that recreate best-practices for instruction (Khachatryan et al 2014), providing elementary and middle school curricula that focuses on fostering deep understanding of core mathematical topics necessary for students' later success in algebra. Having instruction delivered by the computer frees teachers to conduct the sort of targeted interventions with struggling students that research suggests is most effective at improving student learning (Bush and Kim 2014;Waxman and Houston 2012).…”

Designing for Consistent Implementation of a 5th Grade Digital Math Curriculum

“…The Reasoning Mind curricular design was built through Instruction Modeling, the process of examining effective instruction and recreating it through technology. Khachatryan et al (2014) provide a design case for the curricular design, which we summarize as follows: By studying the methods of expert Russian teachers in their traditional classroom settings, Reasoning Mind found that quality mathematics instruction relies on a range of factors including: (a) the coherence of the written curriculum, (b) the activities expected of students, (c) the adaptations of the teacher, (d) the culture of the classroom, and (e) the nature of interactions between teachers and students. To capture these practices, Reasoning Mind's team of expert Russian teachers provided detailed scripts that described the moment-by-moment actions of teachers and students in each lesson.…”

How Immersive Virtual Environments Foster Self-Regulated Learning

“…One increasingly popular strategy for fostering SRL is to use personalized learning within computer-based environments (Azevedo, 2005). An increasing number of personalized learning environments now include various types of support for students in developing SRL skills, including both modeling those skills (Khachatryan et al, 2014), giving regular reports about whether students are demonstrating SRL (Arroyo et al, 2007), and even providing immediate feedback when students demonstrate behaviors associated with poorer SRL (Roll, Aleven, McLaren, & Koedinger, 2007). The challenge of open-ended learning environments such as immersive virtual environments, even environments designed to personalize based on student knowledge, is that learners have to deploy self-regulatory processes and strategies in order to complete tasks and learn complex topics (Azevedo, 2005;Segedy, Kinnebrew, & Biswas, 2015).…”