This SBIR Phase I project will develop new methods for training and assessing the skill of providing and receiving constructive feedback in collaborative environments. The United States higher education system is failing to train collaboration skills, despite companies listing it as one of the most important skills for new hires to have. Past research has identified specific types of feedback that most effectively encourage peers to improve, but today's higher education instructors have no ready means to effectively assess students' abilities to use these peer feedback strategies. This project will develop simple-to-use software tools to help instructors train peer feedback skills and new methods for automatically assessing student ability to provide and receive constructive feedback. By democratizing the training of the collaborative skills most sought after by today's employers, this project aims to reduce underemployment of college graduates and increase the economic output and tax revenues of the United States by building a better trained and more productive workforce.This project will develop an innovative peer-assessment tool coupled with novel natural language processing methods producing accurate measurements of student ability to give and receive constructive feedback based on written feedback. The natural language processing methods will combine word vectors for efficient language processing, a generalizable neural net structure for sentence categorization outlined in existing research literature, and manually defined features based on existing peer feedback research. Research efforts will focus on gathering a large training dataset from partnering higher education classrooms, and iteratively refining a deep-learning natural language processing algorithm to achieve a target 90% accuracy. Automating this assessment process will allow broad and continuous assessment of student collaborative ability without requiring expensive and time-consuming retraining of the United States instructor workforce.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.
