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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.

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.

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.

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.

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