The digital fingerprint of learner behavior: Empirical evidence for individuality in learning using deep learning - Asaf Salman; Giora Alexandron (Computers and Education: Artificial Intelligence)
Personalized learning builds upon the fundamental assumption of uniqueness in learning behavior, often taken for granted. Quite surprisingly, however, the literature provides little to no empirical evidence backing the existence of individual learning behaviors. Driven by curiosity, we challenge this axiom. Our operationalization of a unique learning behavior draws an analogy to a fingerprint – a distinctive trait that sets individuals apart, which we correspondingly termed the ‘Digital Fingerprint of Learner Behavior’ (DFL). If such a thing as DFL truly exists, then given enough fine-grained behavioral data, we argue that it should be possible to model a DFL to a level of discriminability that enables training machine learning models to associate (map) between the (de-identified) digital traces of the same learner in diverse contexts. To test our hypothesis, we experimented with data from 24 MITx massive open online courses (MOOCs) offered via edX between 2014 and 2017. We focused our investigation on contexts where both the content and platform remain constant. A learner's DFL was computed from the learner's activity data within a specific course chapter, as stored in the system's logs. The results show that the mean level of accuracy (across courses) in identifying unseen DFLs is 0.582 (SD=0.173). Using Shapley Additive exPlanations (SHAP), we rank 686 features for their importance in differentiating between DFLs. To the best of our knowledge, this study is the first to provide empirical evidence that learners' behavior is unique to a degree that can distinguish between them on an individual level, similar to the level of identification provided by a fingerprint, and sets a benchmark for the task of DFL identification.