SBIR Phase I: Intelligent Language Learning Environment

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将改善美国目前在外语技能方面的不足，并为130亿美元的全球在线语言学习市场带来关键的创新。目前的语言学习方法主要集中在死记硬背上，而对语言的使用却远远不够。体验式学习的机会，学生有自由流动的对话驱动的兴趣往往不会引入，直到第三或第四学期的语言学习。结果是美国学生毕业时几乎没有能力或动力实际使用外语并保持任何程度的语言流利性。利用新颖的机器学习技术和游戏设计，该项目将让学生在与朋友发短信的同时学习一门语言，并帮助教师在课堂上促进基于对话的学习。该项目将为位于美国的学生提供与地球仪其他国家的学生互动的机会，将教科书抽象化为真实的人，文化和语言。最终，这些机器学习解决方案将为全球12亿学习语言的人提供一个平台，使他们能够使用体验式学习来补充教科书教学，以提高长期的流利性。这个小型企业创新研究（SBIR）第一阶段项目将开发一个最先进的机器学习解决方案，以解决基于对话的语言学习实施中的关键挑战。通过对话学习语言已被先前的研究表明是吸引人和有效的。然而，初学者在早期往往会感到不舒服，因为他们没有信心表达自己。教育工作者可以提供必要的支持，但他们很难给每一个学生个性化的辅导。这个项目将在聊天翻译、字典和语法检查的帮助下快速启动这些初始对话。为了巩固学习，该项目将直接从聊天中生成练习活动，并在这些真实文本对话的背景下提供活动。这个项目将分析文本聊天，并形成关于学生优势和劣势的假设。然后，系统将通过从相同的文本聊天自动生成的评估来测试这些假设。第一阶段的技术目标是：1）自动识别学生文本中的学习目标示例; 2）调整系统生成的和人工编写的评估活动; 3）以更高的准确性预测学生在实践活动中的分数。随着时间的推移，该系统将了解学生的需求，以最大限度地提高学习和参与度。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/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will improve the current US deficiency in foreign language skills and bring critical innovations to the $13 billion global online language learning market. Current language learning methods focus on rote memorization and not nearly enough on using the language. Experiential learning opportunities in which students have free-flowing conversations driven by interests are often not introduced until the third or fourth semester of language study. The result is that American students graduate with little ability or motivation to actually use a foreign language and maintain any degree of language fluency. Using novel machine learning technologies and game design, this project will let students learn a language while texting with their friends and help teachers facilitate conversation-based learning in their classrooms. The project will offer opportunities for students in classes located in the US to interact with students in other countries around the globe, turning textbook abstractions into real people, culture, and language. Ultimately, these machine learning solutions enable a platform that the 1.2 billion individuals learning language worldwide can use to supplement textbook instruction with experiential learning for greater long-term fluency.This Small Business Innovation Research (SBIR) Phase I project will develop a state-of-the-art machine learning solution to address critical challenges in the implementation ofconversation-based language learning. Learning languages through conversation has been shown by previous research to be engaging and effective. However, beginners are oftenuncomfortable in the early days because they aren’t confident in expressing themselves. Educators can provide the needed support, but it is difficult for them to give every studentpersonalized tutelage. This project will jumpstart these initial conversations with the help of in-chat translation, dictionaries, and grammar checking. To cement learning, the project will generate practice activities directly from chats and deliver the activities in the context of those authentic text conversations. This project will analyze the text chats and form hypotheses about student strengths and weaknesses. The system will then test these hypotheses with assessments automatically generated from the same text chats. The Phase I technical objectives are to 1) automatically identify examples of learning objectives in student text 2) align system-generated and human-authored assessment activities and 3) predict student scores on practice activities with increasing accuracy. Over time, the system will understand student needs to maximize learning and engagement.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.