SBIR Phase I: Intelligent Language Learning Environment

这个小企业创新研究（SBIR）第一阶段项目的广泛影响和商业潜力将改善目前美国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侧重于死记硬背，而对语言的使用不够重视。体验式学习的机会，让学生在兴趣的驱使下进行自由流畅的对话，往往是在语言学习的第三或第四学期才引入的。其结果是，美国学生毕业时几乎没有能力或动力去实际使用一门外语，也没有保持某种程度的语言流利。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。该项目将为美国课堂的学生提供与全球其他国家学生互动的机会，将教科书中的抽象概念转化为真实的人物、文化和语言。最终，这些机器学习解决方案为全球12亿学习语言的人提供了一个平台，可以通过体验式学习来补充教科书教学，从而提高长期的流利程度。这个小企业创新研究（SBIR）第一阶段项目将开发最先进的机器学习解决方案，以解决基于会话的语言学习实施中的关键挑战。以前的研究表明，通过对话学习语言是一种吸引人的有效方法。然而，初学者在早期往往会感到不舒服，因为他们对表达自己没有信心。教育工作者可以提供所需的支持，但他们很难给每个学生个性化的指导。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，并在这些真实的文本对话的背景下交付活动。这个项目将分析文本聊天，并形成关于学生的优点和缺点的假设。然后，系统将通过从相同的文本聊天中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学习目标的示例；2)对齐系统生成的和人工编写的评估活动；3)以越来越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，系统将了解学生的需求，以最大限度地提高学习和参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.