The Company You Keep: Refining Neural Epistemic Network Analysis

Collaborative problem-solving (CPS) is defined as an inherently sociocognitive phenomena. Despite this, extant learning analytic techniques tend to focus on either the social or cognitive aspects without explicitly considering their interaction. Prior work developed Neural Epistemic Network Analysis (NENA), which used a combination of deep learning methods to simultaneously model the social and cognitive aspects of CPS; however, the method had several limitations. The refined version of NENA presented here addresses these limitations by (a) introducing a simplified autoencoder deep learning architecture; (b) using a combination of social and epistemic networks as input to preserve interpretability in terms of social and cognitive factors; and (c) introducing an isometry loss function to ensure downstream statistical tests are meaningful. We found that the refined version of NENA is able to achieve high performance on criteria we would expect from a network analytic technique in the context of learning analytics: interpretability, goodness of fit, orthogonality and isometry; and discriminatory power. We also demonstrated that this method was comparable in performance to a more traditional learning analytic technique, Epistemic Network Analysis (ENA), while providing information that ENA did not. The results suggest that NENA could be a useful method for exploring the cognitive interactions of a given individual's social network and thus the influences their network exerts upon them.