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.

This study builds on prior research by leveraging natural language processing (NLP), click-stream analyses, and survey data to predict students’ mathematics success and math identity (namely, self-concept, interest, and value of mathematics). Specifically, we combine NLP tools designed to measure lexical sophistication, text cohesion, and sentiment with analyses of student click-stream data within an online mathematics tutoring system. We combine these data sources to predict elementary students’ success within the system as well as components of their math identity as measured though a standardized survey. Data from 147 students was examined longitudinally over a year of study. The results indicated links between math success and non-cognitive measures of math identity. Additionally, the results indicate that math identity was strongly predicted by click-stream variables and the production of more lexically sophisticated and cohesive language. In addition, significant variance in math identity was explained by affective and cognitive variables. The results indicate that NLP and click-stream data can combine to provide insights into non-cognitive constructs such as math identity.

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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.

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