SBIR Phase I: A mobile, device-based, screening tool for assessing K-6 students’ cognitive and motor skills via machine learning handwriting analysis

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是改善K-6学生的认知和运动技能的评估。更具体地说，该项目将开发和执行一种新颖的，客观的，机器学习驱动的方法来分析学生的手写能力，认知和运动技能的措施的可行性测试。通过使用智能设备应用程序，最终用户（教师，助手和家长）将能够拍摄学生的笔迹照片，并立即获得有关熟练程度，笔迹错误类型和有针对性的干预建议的结果。考虑到生成手写样本所需的视觉运动、精细运动和高阶认知技能，以及高达30%的学生有困难的事实，需要新的更好的检测方案。由于COVID-19危机，虚拟学习环境的使用越来越多，识别认知和运动技能缺陷变得尤为重要。学生们越来越多地与计算机进行交互，而教师们越来越少地接触到手写作业。这个小型企业创新研究（SBIR）第一阶段项目的重点是开发机器学习（ML）算法，以生成高度准确、快速和客观的手写熟练度预测。这些算法寻求预测手写错误子类型。以前从未对手写图像进行过ML分析。通过使用数据注释方案，智能设备应用程序将在获取单个手写句子的单个照片后创建和访问高度敏感和特定于年级的算法。这项技术被设想为一种通用的筛选工具，在每个学年开始时使用，以识别笔迹熟练程度低于标准的学生。手写能力的实时分析将允许早期识别和早期干预，以提高学生的成绩，并为学区节省成本。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase 1 project is to improve the assessment of cognitive and motor skills in K-6 students. More specifically, this project will develop and perform feasibility testing of a novel, objective, and machine learning-driven approach to analyzing student handwriting proficiency, a measure of cognitive and motor skills. Through the use of a smart device application, end-users (teachers, aides, and parents) will be able to take a photo of a student's handwriting and receive immediate results regarding proficiency, handwriting error types, and targeted intervention suggestions. Given the myriad of visual motor, fine motor, and higher-order cognitive skills needed to generate a handwriting sample and the fact that up to 30% of students have difficulties, there is a need for new and better detection schemes. The identification of cognitive and motor skill deficiencies is becoming especially important with the increased use of virtual learning environments due to the COVID-19 crisis. Students are interfacing more with computers, and teachers have decreased access to handwriting assignments.This Small Business Innovation Research (SBIR) Phase 1 project focuses on developing machine learning (ML) algorithms to generate highly accurate, rapid, and objective predictions of handwriting proficiency. These algorithms seek to predict the handwriting error sub-type. ML analysis of handwriting images has never been done before. Through the use of data annotation schemes, highly sensitive and grade-specific algorithms will be created and accessed by a smart device application following the acquisition of a single photo of a single handwritten sentence. This technology is envisioned as a universal screening tool to be used at the beginning of each school year to identify students with subpar handwriting proficiency. The real-time analysis of handwriting proficiency will allow for earlier identification and earlier interventions to improve student outcomes and deliver cost savings to school districts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.