SBIR Phase I: PeTeS: Personalized Text Simplification For English Language Learners

这个SBIR第一阶段项目旨在开发一种新颖的文本简化技术，将个性化文本的阅读水平的学生。在包容性和综合性的K- 12教室中需要这种工具，以满足不同阅读水平的学生的需要。这尤其适用于英语学习者（Els）、国际学生和有学习障碍的特殊教育学生，如阅读障碍、多动症和其他阻碍他们阅读能力的认知障碍。现有的工具提供了一种通用的解决方案，无法控制简化过程。拟议中的个性化文本简化器（PeTeS）将使文本适应个别学生的阅读水平，从而填补了市场上急需的空白。由于超过60%的美国K-12学生的阅读低于年级水平，拟议技术的更广泛影响将是提高多样化人口的识字率。使用PeTeS的预期结果将是提高词汇习得和提高英语学习者和国际学生对文本的阅读和理解。使用PeTeS进行阅读可能会带来更好的成绩和更高的毕业率。PeTeS还将节省教师的时间，不必为学生解释课文或翻译。PeTeS将补充教师，并为英语学习者和国际学生提供额外的支持，特别是在内容领域，这些学生很少得到支持。拟议的PeTeS被设想为个性化教学的自适应阅读助手。PeTeS的关键创新在于应用自然语言处理算法来执行自动文本简化，以动态地满足特定的阅读水平-简化被定义为以最大化学生理解其内容的可能性的方式对任何给定文本进行改编。这里的关键思想是用一个被假定为已知的词来替换一个被假定为学生不知道的词，即，替换的标准是学生？的知识，而不是复杂性这个词。为此，所提出的PeTeS技术将维护代表学生的阅读水平的学生知识模型。PeTeS将依靠该模型不仅简化文本到学生的水平，提高理解力，而且还挑战学生在阅读任何他们需要阅读的东西时学习不熟悉的单词。该奖项反映了NSF的法定使命，并被认为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来支持。

This SBIR Phase I project seeks to develop a novel text simplification technology that will personalize text to the reading level of students. There is a demand for such tools in inclusive and integrated K- 12 classrooms to cater to the needs of students with varying reading levels. This is especially pertinent to English language learners (Els), international students, and Special Education students with learning disabilities such as dyslexia, ADHD, and other cognitive impairments that impede their reading abilities. Existing tools offer one-size-fits-all solutions offering no control over the simplification process. The proposed Personalized Text Simplifier (PeTeS) will adapt texts to the reading levels of individual students, thereby filling a much-needed gap in the marketplace. With over 60% of U.S. K-12 students reading below grade level, the broader impact of the proposed technology will be in improving the literacy of a diverse population. The expected outcomes of using PeTeS will be improved vocabulary acquisition and improved reading and comprehension of texts by ELs and international students. Using PeTeS for reading may lead to better grades and higher graduation rates. PeTeS will also save teachers time on not having to explain texts or translate for students. PeTeS, will complement the instructor and provide additional support to ELs and international students, especially, in content areas, where these students have very little support.The proposed PeTeS is envisioned to be an adaptive reading assistant for personalizing instruction. The key innovation of PeTeS is in the application of natural language processing algorithms to perform automatic text simplification to dynamically meet specific reading levels - simplification being defined as an adaptation of any given text in a way that maximizes the likelihood that the student will understand its content. The key idea here is to replace a word that is presumed to be not known by the student with a word that is presumed to be known, i.e., the replacement criteria is the student?s knowledge and not the word complexity. Towards that, the proposed PeTeS technology will maintain a student knowledge model representing the reading level of the student. PeTeS will rely on the model not only to simplify the text to the student's level and improve comprehension, but also to challenge the student to learn unfamiliar words while reading whatever they need to read.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.