TextInContext: On the Way to a Framework for Measuring the Context-Sensitive Complexity of Educationally Relevant Texts—A Combined Cognitive and Computational Linguistic Approach

We develop a framework for modeling the context sensitivity of text interpretation. As a point of reference, we focus on the complexity of educational texts. To open up a broader basis for representing phenomena of context sensitivity, we integrate a learning theory (i.e., the Cognitive Load Theory) with a theory of discourse comprehension (i.e., the Construction Integration Model) and a theory of cognitive semantics (i.e., the theory of Conceptual Spaces). The aim is to construct measures that view text complexity as a relational attribute by analogy to the relational concept of meaning in situation semantics. To this end, we reconstruct the situation semantic notion of relational meaning from the perspective of a computationally informed cognitive semantics. The aim is to prepare the development of measurements for predicting learning outcomes in the form of positive or negative learning. This prediction ideally depends on the underlying learning material, the learner’s situational context, and knowledge retrieved from his or her long-term memory, which he or she uses to arrive at coherent mental representations of the underlying texts. Finally, our model refers to machine learning as a tool for modeling such memory content. In this way, the chapter integrates approaches from different disciplines (linguistic semantics, computational linguistics, cognitive science, and data science).