Towards a cost-efficient open-source infrastructure for physiological multimodal learning analytics

Sudden moments of insights have shown to be associated with a steeper trajectory towards solution attainment compared to an incremental progress. One possible explanation is that solutions may emerge from unconscious processing and enter conscious awareness in a relatively integrated form. This makes it challenging to explore and study these “Aha!” moments, as insights are commonly referred to. One possible way of exploring them is to collect data from multiple signals in order to both study behaviour as well as context. Such multimodal data collection can quickly become expensive, which is why this study took a two fold approach to the problem. The first was the study of “Aha!” moments during a creative problem solving task. And the second was to explore if a cost-efficient and open-source infrastructure could be developed to collect the needed data. After developing and testing the infrastructure we identified moments of insight to not only be a single cognitive state, but rather three three different cognitive states, expressing inherently different neural patterns.