INFACT

Including Neurodiversity in Foundational and Applied Computational Thinking

Lead Staff:
Jodi Asbell-Clarke
Project Staff:
Mia Almeda
Erin Bardar
Ibrahim Dahlstrom-Hakki
Teon Edwards
Kelly Paulson
Tara Robillard
Elizabeth Rowe

Introduction

EdGE at TERC is working with a team of leading researchers and practitioners to design, develop, and implement a comprehensive and inclusive program for computational thinking in grades 3-8. This program will embed supports for attention, metacognition, and social-emotional learning and use educational data mining to make supports and pacing customizable for the strengths and struggles of each unique learner.

Summary

Funded by the US Department of Education’s Education Innovation and Research Program, INFACT is a consortium of leading researchers and practitioners in CT education. EdGE at TERC is leading the team in the design, development, implementation and research of a comprehensive set of teaching and learning materials for inclusive computational thinking (CT).

Project INFACT

The INFACT system, infusing CT in elementary and middle school STEM, will include curriculum, assessments, and professional development. For the elementary grades, INFACT will introduce CT practices such as problem decomposition, pattern recognition, abstraction, and algorithm design through games, puzzles, and classroom activities to build a strong conceptual foundation. For specialized classes that begin in middle school (STEM and other subjects), INFACT will also apply CT in project-based learning activities.

INFACT pays particular attention to the inclusion of a wide range of learners, including neurodiverse learners. INFACT recognizes that each learner brings a unique set of assets and deficits to every learning situation. By adapting to those strengths and weaknesses and by providing cognitive and social-emotional supports, we aim to leverage talents needed for the workforce of the future.

Research Activity

INFACT includes iterative design research for the first two years, followed by a pilot year and a full-scale efficacy study. In gathering a range of CT measures for this study, the INFACT team is exploring the use of novel methods of research, including eye-tracking and facial recognition using web-based cameras, screen capture, and audio/video. These methods will help us understand where and when students are focusing their attention while using new and developed materials, and what INFACT materials promote excitement and engagement. 

In addition, our team is designing automated methods for the detection of productive and unproductive persistence including measures of frustration and boredom. Ultimately, these detectors will be used in adaptivity models that customize the delivery of the experience based upon the detected state of the learner.