论文标题
Kwame：在线SuaCode课程的双语AI AI助教
Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses
论文作者
论文摘要
介绍性的动手课程，例如基于智能手机的编码课程，Suacode需要大量支持学生来实现学习目标。在线环境使获得协助变得更加困难，尤其是由于COVID-19，最近的环境更加困难。鉴于SuaCode学生的多语言背景 - 在这项工作中，四十个非洲国家的学习者 - 大多数是英语或法语国家的学习者，我们开发了双语的人工智能（AI）助教（TA） - Kwame- kwame-为学生提供了英语和法语中Suacode Course的学生编码问题的答案。夸梅（Kwame）是一个基于句子 - 基于问题的问题 - 索问题（QA）系统，我们使用了从课程的测验，课程笔记和学生的问题中创建的问答对培训和评估了离线对。 Kwame通过余弦相似性找到了与该问题最相似的段落。我们将系统与TF-IDF和通用句子编码器进行了比较。我们的结果表明，在课程数据上进行微调并返回前3和5的答案提高了准确性结果。 Kwame将使学生可以轻松地在Suacode课程中快速准确地回答问题。
Introductory hands-on courses such as our smartphone-based coding course, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of SuaCode students - learners across 42 African countries that are mostly Anglophone or Francophone - in this work, we developed a bilingual Artificial Intelligence (AI) Teaching Assistant (TA) - Kwame - that provides answers to students' coding questions from SuaCode courses in English and French. Kwame is a Sentence-BERT (SBERT)-based question-answering (QA) system that we trained and evaluated offline using question-answer pairs created from the course's quizzes, lesson notes and students' questions in past cohorts. Kwame finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that fine-tuning on the course data and returning the top 3 and 5 answers improved the accuracy results. Kwame will make it easy for students to get quick and accurate answers to questions in SuaCode courses.
