Selection-based Question Answering of an MOOC
This work addresses the problem of timely support for students in a large-scale online robotics competition, though it is incremental as it adapts existing methods to a specific domain.
The paper tackled the challenge of real-time question answering in a robotics MOOC discussion forum by applying deep learning, specifically comparing Transformer-based embeddings like BERT to Word2Vec and proposing a weighted similarity metric, which reduced the minimum response time from 21 minutes to 0.3 seconds.
e-Yantra Robotics Competition (eYRC) is a unique Robotics Competition hosted by IIT Bombay that is actually an Embedded Systems and Robotics MOOC. Registrations have been growing exponentially in each year from 4500 in 2012 to over 34000 in 2019. In this 5-month long competition students learn complex skills under severe time pressure and have access to a discussion forum to post doubts about the learning material. Responding to questions in real-time is a challenge for project staff. Here, we illustrate the advantage of Deep Learning for real-time question answering in the eYRC discussion forum. We illustrate the advantage of Transformer based contextual embedding mechanisms such as Bidirectional Encoder Representation From Transformer (BERT) over word embedding mechanisms such as Word2Vec. We propose a weighted similarity metric as a measure of matching and find it more reliable than Content-Content or Title-Title similarities alone. The automation of replying to questions has brought the turn around response time(TART) down from a minimum of 21 mins to a minimum of 0.3 secs.