FATHOM: A Neural Network-based Non-verbal Human Comprehension Detection System for Learning Environments

O'Shea, JD, Crockett, K, Bandar, Z and Buckingham, F (2016) FATHOM: A Neural Network-based Non-verbal Human Comprehension Detection System for Learning Environments. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2014.

Preview

Available under License In Copyright. Download (410kB) | Preview

Abstract

This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written and spoken language. Therefore, a large niche exists for exploring human comprehension detection from a non-verbal behavioral perspective using artificially intelligent computational models such as neural networks. In this paper, FATHOM was applied to a video-recorded exploratory study containing a learning task designed to elicit high and low comprehension states from the learner. The learning task comprised of watching a video on termites, suitable for the general public and an interview led question and answer session. This paper describes how FATHOM’s comprehension classifier artificial neural network was trained and validated in comprehension detection using the standard backpropagation algorithm. The results show that high and low comprehension states can be detected from learner’s non-verbal behavioural cues with testing classification accuracies above 76%.