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.
@article{arxiv.2010.11387,
title = {Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses},
author = {George Boateng},
journal= {arXiv preprint arXiv:2010.11387},
year = {2021}
}
Comments
6 pages. Accepted and presented at NeurIPS 2020 workshop (Black in AI) and AIED 2021 (international conference on AI in Education)