Dr. Jiliang Tang has been awarded an NSF grant

Here is the abstract:


This project aims to serve the national interest by improving preservice teachers? evidence-based teaching practices through an interactive program with personalized feedback. Teachers? conceptual understanding of mathematics as well as their understanding of how students learn, and their repertoire of instructional tools and practices, are essential for implementing teaching practices that will lead students to develop command of concepts. Yet preservice teachers may have varying degrees of access to learning opportunities, depending on their location or the teacher preparation program in which they enroll. This project will address this issue through a program that is accessible to elementary and middle grade preservice teachers anywhere and at any time focused on teaching of ratios and proportional relationships, an area of persistent challenge for learners. Adaptation of a protype that was found effective in improving in-service teachers? mathematics teaching and students? mathematics learning is a basis for this work. Preparation of a teaching workforce with strengthened content-specific knowledge and teaching skills can create learning environments for students to attain command of STEM concepts. This project hopes to advance understanding of preservice teachers? learning and contribute new knowledge about how an interactive program that uses artificial intelligence can improve preservice teachers? knowledge and practices.


The goal of the project is to enhance preservice teachers? mathematical content knowledge (CK), pedagogical content knowledge (PCK), and teaching skills through the development of a program that uses natural language processing to provide instant, personalized feedback. Teachers? CK and PCK have been identified as essential for teachers to implement teaching practices that lead students in developing deep conceptual knowledge. Although a prototype of the program has been found effective in improving in-service teachers? practices and their students? learning, the issue of how a similar program might be designed for preservice teachers, who differ from in-service teachers in terms of their knowledge and experience, has not been examined. The extent to which natural language processing can reliably detect preservice teachers? responses will also be researched in the project. The program will be developed and refined through an interactive design cycle, based on feedback from instructors of mathematics methods courses and from preservice teachers. The effectiveness of the program on preservice teachers? knowledge and teaching practices will then be examined through a randomized controlled trial with a sample of 200 preservice teachers drawn from at least eight universities across the nation. The following questions will be investigated: How well does the intelligent, interactive computer-based program with just-in-time feedback enhance preservice teachers? CK and PCK? and What is the impact of the intelligent, interactive computer-based program with just-in-time feedback on preservice teachers? teaching of ratios and proportional relationships? An Advisory Board of experts will provide independent feedback throughout the three phases of the project. The final version of the program will be made freely available online. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning Track, the program supports the creation, exploration, and implementation of promising practices and tools.


(Date Posted: 2023-08-23)