Reasoning Mind

Earth, TX, United States

Reasoning Mind

Earth, TX, United States
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A grant to the West Virginia Regional Education Centers will provide Reasoning Mind's new early learning program to over 3,000 students in 2017-2018.

Crossley S.,Georgia State University | Kostyuk V.,Reasoning Mind
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

This study examines links between natural language processing and its application in math education. Specifically, the study examines language production and math success in an on-line, blended learning math program. Unlike previous studies that have relied on correlational analyses between linguistic knowledge tests and standardized math tests or compared math success between proficient and non-proficient speakers of English, this study examines the linguistic features of students’ language production while e-mailing a virtual pedagogical agent. In addition, the study examines a number of non-linguistic features such as grade and objective met within the program. The findings indicate that linguistic features related to the use of standardized language use explain around 8% of math success. These linguistic features outperform non-linguistic features. © Springer International Publishing AG 2017.

Sedlacek L.,Reasoning Mind | Kostyuk V.,Reasoning Mind | Labrum M.,Reasoning Mind | Mulqueeny K.,Reasoning Mind | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Studies have shown student performance in virtual learning environments is improved by the presence of a virtual pedagogical agent, particularly when the agent is voiced by a human voice. Furthermore, studies have shown students achieve higher learning outcomes when their teacher is a content expert in the material being taught. This study examines the question at the intersection of these two domains: Do students achieve higher learning outcomes in a virtual learning environment if the actor voicing the virtual agent is a content expert in the material being taught? The analysis found no evidence of such an effect, although more research should be conducted before firm conclusions are drawn. © Springer International Publishing AG 2017.

Ocumpaugh J.,Worcester Polytechnic Institute | Baker R.S.J.D.,Columbia University | Gaudino S.,Reasoning Mind | Labrum M.J.,Reasoning Mind | Dezendorf T.,Reasoning Mind
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

This study presents Quantitative Field Observations (QFOs) of educationally relevant affect and behavior among students at three schools using Reasoning Mind, a game-based software system designed to teach elementary-level mathematics. High levels of engagement are observed. Possible causes for these high levels of engagement are considered, including the interactive pedagogical agent and other design elements. © 2013 Springer-Verlag Berlin Heidelberg.

Kostyuk V.,Reasoning Mind | Mingle L.A.,Reasoning Mind | Mulqueeny K.,Reasoning Mind
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Reasoning Mind Genie 3 is a recently developed intelligent tutoring system that simulates a small-group tutoring session. Elements of the system’s design include interactions with peer and tutor pedagogical agents, embodiment and attention-focusing to reduce cognitive load, precise synchronization of voiced speech and on-screen movement, and complementarity rather than duplication of speech and on-screen text. The system is used to closely model the daily instructional practice and pedagogical decisions of expert classroom teachers. A pilot of the system in 2013-2014 showed promising results on a curriculum aligned assessment and an independent assessment (ITBS), and quantitative observations of student behavior and affect revealed a high percentage of time spent on-task and in engaged concentration. © Springer International Publishing Switzerland 2015.

Mulqueeny K.,Reasoning Mind | Mingle L.A.,Reasoning Mind | Kostyuk V.,Reasoning Mind | Baker R.S.,Columbia University | Ocumpaugh J.,Worcester Polytechnic Institute
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Student engagement indicators, such as behavior and affective states, are known to impact learning. This study uses an established quantitative field observation method to evaluate engagement during students’ use of a new version of an online learning system (Reasoning Mind’s Genie 3). Improvements to Genie 3’s design intended to increase engagement include: using virtual small-group tutoring environment, separating text and speech, and using indicators to focus students’ attention. In this study, Genie 3 classrooms outperformed a traditional classroom on key indicators of engagement, including time on-task, engaged concentration, and boredom. These results have important implications for further improvements to Reasoning Mind, for the design of other online learning systems, and for general pedagogical practices. © Springer International Publishing Switzerland 2015.

Miller W.L.,Reasoning Mind | Baker R.S.,Columbia University | Labrum M.J.,Reasoning Mind | Petsche K.,Reasoning Mind | And 2 more authors.
ACM International Conference Proceeding Series | Year: 2015

Among the most important tasks of the teacher in a classroom using the Reasoning Mind blended learning system is proactive remediation: dynamically planned interventions conducted by the teacher with one or more students. While there are several examples of detectors of student behavior within an online learning environment, most have focused on behaviors occurring fully within the context of the system, and on student behaviors. In contrast, proactive remediation is a teacher-driven activity that occurs outside of the system, and its occurrence is not necessarily related to the student's current task within the Reasoning Mind system. We present a sensor-free detector of proactive remediation, which is able to distinguish these activities from other behaviors involving idle time, such as on-task conversation related to immediate learning activities and off-task behavior.

Khachatryan G.A.,Reasoning Mind | Romashov A.V.,Reasoning Mind | Khachatryan A.R.,Reasoning Mind | Gaudino S.J.,Reasoning Mind | And 3 more authors.
International Journal of Artificial Intelligence in Education | Year: 2014

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. © 2014 International Artificial Intelligence in Education Society.

Miller W.L.,Reasoning Mind | Baker R.S.,Columbia University | Rossi L.M.,Georgia Institute of Technology
Technology, Knowledge and Learning | Year: 2014

As students work through online learning systems such as the Reasoning Mind blended learning system, they often are not confined to working within a single educational activity; instead, they work through various different activities such as conceptual instruction, problem-solving items, and fluency-building games. However, most work on assessing student knowledge using methods such as Bayesian Knowledge Tracing has focused only on modeling learning in only one context or activity, even when the same skill is encountered in multiple different activities. We investigate ways in which student learning can be modeled across activities, towards understanding the relationship between different activities and which approaches are relatively more successful at integrating information across activities. However, we find that integrating data across activities does not improve predictive power relative to using data from just one activity. This suggests that seemingly identical skills in different activities may actually be cognitively different for students. © 2014 Springer Science+Business Media Dordrecht.

News Article | February 17, 2017

The Governor visited a kindergarten classroom at Robertson Elementary, which is using an exciting new online math curriculum from education nonprofit Reasoning Mind.

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