Based on the established framework of Cognitive Load Theory, the presented research focuses on the inspection of cognitive load factors in an interrupted learning task. The task itself is inspired from basic cognitive research and demands participants to learn abstract symbol combinations of varying complexity. In addition, they have to deal with interruptions while performing the task. Experimental results indicate the influence of task complexity on how interruptions effect learning performance. However, questions on underlying learner cognition persist, rising the need for a more in-depth way of examination. For this purpose, a cognitive model within the cognitive architecture ACT-R is developed to clarify cognitive processes and mechanisms within different conditions of the task. Preliminary results from a first model for the easy task condition already indicate some fit between human and model data. Modeling work continues with adjusting the current model and implementing a model for the difficult task condition.

Learning constitutes an essential part of human experienceover the life course. Independent of the domain, it ischaracterized by changes in performance. But what cognitivemechanisms are responsible for these changes and how dosituational features affect the dynamics? To inspect that inmore detail, this paper introduces a cognitive modelingapproach that investigates performance-related changes inlearning situations. It leverages the cognitive architectureACT-R to model learner behavior in an interrupted learningtask in two conditions of task complexity. Comparisons withthe original human dataset indicate a good fit in terms of bothaccuracy and reaction times. Although interruption effects aremore obvious in the human data, they are prevalent as well inthe model. Furthermore, the model can map the learningeffects, particularly in the easy task condition. Based on theexisting mapping of ACT-R module activity with fMRI data,simulated neural activity is computed to investigate underlyingcognitive mechanisms in more detail. The resulting evidenceconnects learning and interruption effects in both taskconditions with activation-related patterns to explain changesin performance.