SGER III-CXT: Integrating Computer Science Techniques into Differentiated Instruction of Mathematical Word Problem Solving

差异化教学在今天的包容性课堂中发挥着关键作用，以满足学生个体的不同需求，通过提供适合学生需求的不同切入点和学习任务，允许所有学生访问相同的课堂课程。本研究旨在构建具有以下功能的差异化数学应用题解决教学体系。首先，该系统维护一个由预先定义的模板生成或从学生/教师共享的教学材料池；通过拟议的统计自然语言处理技术，将自动从教学材料中提取可读性和无关信息的噪音水平等特征。其次，该系统提供计算机辅助教学，培养学生分析和解决数学应用题的能力。第三，它使形成性评价能够监控学生的进步。第四，利用学生成绩驱动的推荐算法，为特定的学生提供差异化的教材推荐。拟议的工作包括计算机科学技术的前沿研究。将设计一个联合统计学习算法，在一个统一的框架内确定现有教材的可读性、单词难度和句法复杂性。设计了一种关系学习算法，用于从数学应用题中不相关信息的句子中识别出具有相关信息的句子。

Differentiated instruction plays a critical role in today's inclusive classrooms to meet the diverse needs of individual students for allowing all students to access the same classroom curriculum by providing different entry points and learning tasks that are tailored to students' needs. The proposed research is to construct a differentiated instructional system of mathematical word problem solving with the following functionalities. First, the system maintains a pool of instructional materials generated by pre-defined templates or shared from students/teachers; features such as readability and the noise level of irrelevant information will be automatically extracted from instructional materials by proposed statistical natural language processing techniques. Second, the system provides computer-assisted instruction to train students' abilities for analyzing and solving mathematical word problems. Third, it enables formative evaluation to monitor students' progress. Fourth, the system provides the recommendation of differentiated instructional materials for a specific student by utilizing a student performance-driven recommendation algorithm. The proposed work includes cutting-edge research for computer science techniques. A joint statistical learning algorithm will be designed for identifying levels of readability, word difficulty, and syntactic complexity of available instructional materials in a unified framework. A relational learning algorithm will be designed for identifying sentences with relevant information from sentences with irrelevant information in a mathematical word problem.