automaTA: Human-Machine Interaction for Answering Context-Specific Questions

Abstract

When online learners have questions that are related to a specific task, they often use Q&A boards instead of web search because they are looking for context-specific answers. While lecturers, teaching assistants, and other learners can provide context-specific answers on the Q&A boards, there is often a high response latency which can impede their learning. We present automaTA, a prototype that suggests context-specific answers to online learners' questions by capturing the context of the questions. Our solution is to automate the response generation with a human-machine mixed approach, where humans generate high-quality answers, and the human-generated responses are used to train an automated algorithm to provide context-specific answers. automaTA adopts this approach as a prototype in which it generates automated answers for function-related questions in an online programming course. We conduct two user studies with undergraduate and graduate students with little or no experience with Python and found the potential that automaTA can automatically provide answers to context-specific questions without a human instructor, at scale.