About the Project

Overview

 

Virtual learning environments are an increasingly important component of individualized learning in STEM domains. New technologies (including biometry and neuroimaging) provide new opportunities to unobtrusively measure student engagement and learning at scale. This project utilizes these technologies to provide foundational knowledge of the ways in which measures of implicit learning from lab settings, ubiquitous sensors, and big data might be linked to explicit learning to develop games and online educational systems that are adaptive to diverse learners. 

Extended Abstract

 

Investigators from TERC, Landmark College, and the Massachusetts Institute of Technology will collaborate to examine the relationships among: (1) patterns of play in a digital game ("Impulse"); (2) student attention (measured from eye- and head-tracking devices); and (3) student learning about Newton's first and second law. The researchers will collect measures of student engagement and learning outcomes embedded in the game. Subjects will comprise a neurodiverse group of students including regular undergraduates and those with Attention Deficit Hyperactivity Disorder and/or Autism Spectrum Disorder. The researchers will develop a model of visual attention and patterns of play, examining the extent to which eye movements are allocated strategically to objects of relevance to the current game state as a student learns in the game. They will then link the initial model with measures of student engagement and conceptual understanding of relevant physical science constructs to refine the model. The refined model will be used to develop a modified game based on the players' attention, and a prototype of the modified game will be tested. The final phase of the research will be a within-subject design with the adaptive version versus the normal version of the game across learners with different profiles of disability.

Research Design

 

The project uses a cross-sectional research design and will generate evidence that is associative/correlational [quasi-experimental] and causal [experimental]. Original data are being collected on undergraduate students using observation [videography, Web logs], survey research, and eye-tracking data as well as other attentional signals.

Investigators from TERC, Landmark College, and the Massachusetts Institute of Technology will collaborate to examine the relationships among: (1) patterns of play in a digital game ("Impulse"); (2) student attention; and (3) student learning about Newton's first and second law. The researchers will collect measures of student engagement and learning outcomes embedded in the game. Subjects will comprise a neurodiverse group of students including regular undergraduates and those with Attention Deficit Hyperactivity Disorder and/or Autism Spectrum Disorder. The researchers will develop a model of visual attention and patterns of play, examining the extent to which eye movements are allocated strategically to objects of relevance to the current game state as a student learns in the game. They will then link the initial model with measures of student engagement and conceptual understanding of relevant physical science constructs to refine the model. The refined model will be used to develop a modified game based on the players' attention, and a prototype of the modified game will be tested. The final phase of the research will be a within-subject design with the adaptive version versus the normal version of the game across learners with different profiles of disability.

A variety of machine learning algorithms will be employed to train classifiers on short-term longitudinal data, producing predictions of subject actions and outcomes. Out-of-sample validation methods will be used to avoid overfitting.