SBIR Phase I: Intelligent Language Learning Environment

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将改善美国目前外语技能的缺陷，并为价值 130 亿美元的全球在线语言学习市场带来关键创新。当前的语言学习方法侧重于死记硬背，而对语言的运用还不够。学生在兴趣驱动下进行自由对话的体验式学习机会通常要到语言学习的第三或第四学期才会引入。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程度。该项目利用新颖的机器学习技术和游戏设计，让学生在与朋友发短信的同时学习语言，并帮助教师在课堂上促进基于对话的学习。该项目将为美国班级的学生提供与全球其他国家学生互动的机会，将教科书上的抽象内容转化为真实的人物、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，可以用体验式学习来补充教科书教学，以提高长期流利程度。这个小型企业创新研究 (SBIR) 第一阶段项目将开发最先进的机器学习解决方案，以解决实施基于对话的语言学习中的关键挑战。先前的研究表明，通过对话学习语言是有吸引力且有效的。然而，初学者在早期往往会感到不舒服，因为他们没有自信表达自己。教育工作者可以提供所需的支持，但他们很难为每个学生提供个性化的指导。该项目将借助聊天翻译、词典和语法检查来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提供这些活动。该项目将分析文本聊天并形成有关学生优势和劣势的假设。然后，系统将通过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学习目标的示例 2) 协调系统生成的评估活动和人工编写的评估活动以及 3) 更加准确地预测学生练习活动的分数。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度。该奖项反映了 NSF 的法定使命，并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，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.