Cognitive Learning Analytics

Abstract

Cognitive Learning Analytics (CLA) is an interdisciplinary domain that combines cognitive science and learning analytics to interpret and enhance human learning through theoretically grounded data analysis. It integrates learning analytics with models of cognition to support theoretically grounded interpretation of learner data. Learning analytics, since its inception in 2011, has developed as a research field and applied practice, focusing on “the measurement, collection, analysis, and reporting of data about learners and their contexts.” It focuses on understanding and optimizing learning processes and environments by leveraging large-scale, multimodal educational data. Cognitive science, in parallel, provides established theories of human learning, memory, attention, and metacognition. CLA links observable behaviors with theoretically defined cognitive mechanisms. Through the integration of cognitive theories and computational techniques, CLA models how learners process information, make decisions, and construct knowledge in digital learning environments. CLA employs diverse data sources—including clickstream logs, eye tracking, biometric signals, and linguistic traces—to infer learners’ cognitive and affective states. These inferences inform adaptive learning systems, personalized feedback mechanisms, and intelligent tutoring tools that respond dynamically to the learner’s mental workload, engagement, or metacognitive strategies.