Inferring learning from big data:The importance of a transdisciplinary and multidimensional approach

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention

Identity-specific predictions and implicit measures of agency

ur sense of agency is thought to arise from the predictive nature of the action system. While previous research supports the role of motor-specific identity prediction in the sense of agency, it remains unclear whether identity-specific predictions (e.g., the pitch of a tone) that are not uniquely associated with specific motor responses also have a significant role. In the present study, we recorded EEG activity during an interval estimation task to assess the impact of these identity-specific predictions on intentional binding, N1 suppression, and the P3b component. Intentional binding was found for all tones that followed self-made actions, regardless of identity-specific predictability of the tone (i.e., the probability of that specific tone following the action). For the N1 component, consequent tones that followed any preceding event, whether it was an action or initial control tone, resulted in N1 suppression; however, this N1 suppression was not significantly influenced by identity-specific predictions. In contrast, the P3b component was significantly influenced by identity-specific predictions, with a significantly larger P3b elicited by more unexpected tones (i.e., prediction-incongruent tones) than expected tones (i.e., prediction-congruent tones). The overall P3b response was also larger for tones following self-made actions. Based on these P3b findings, it appears that higher-cognitive processes are needed to track violations of identity-specific prediction when a single motor command elicits different sensory events. In conclusion, identity-specific predictions that are not associated with specific motor responses have a minimal impact on implicit measures of agency such as intentional binding and N1 suppression. (PsycINFO Database Record (c) 2015 APA, all rights reserved