The Cognitive and Metacognitive Educational Systems (MCES) AAAI symposium, held in November 2010, was the second edition of this successful AAAI symposium. The idea for the symposium stemmed from several theoretical, conceptual, empirical, and applied considerations about the role of metacognition and self-regulation when learning with computer-based learning environments (CBLEs). A related goa...

Metacognition relative to medical decision making has been poorly investigated to date. However, beliefs about methods of decision making (metacognition) play a fundamental role in determining the efficiency of the decision itself. In the present study, we investigated a set of beliefs that physicians develop in relation to the modes of making decisions in a professional environment. The Solomo...

Studies of the conceptual structures that support spatial cognition, suggest a common basis for spatial and linguistic memory. Furthermore, studies of route descriptions provide evidence that selecting crucial information units in these directions is based on metacognitive knowledge that is largely independent of a specific environment. In this paper, we investigated spatial memory as revealed ...

We know that metacognitive students are successful in school (Sternberg Instructional Science 26:127–140, 1998). However, despite the recognition of the role of metacognition in student success, limited research has been done to explore teachers’ explicit awareness of their metacognition and their ability to think about, talk about, and write about their thinking (Zohar Teaching and Teacher Edu...

Past research on feedback in computer-based learning environments has shown that corrective feedback helps immediate learning, whereas guided and metacognitive feedback help in gaining deep understanding and developing the ability to transfer knowledge. Feedback becomes important in discovery learning environments, where novice students are often overwhelmed by the cognitive load associated wit...

We discuss metacognitive modelling as enhancement to cognitive modelling and computing. Metacognitive control mechanisms should enable AI systems to self-reflect, reason about their actions, and to adapt to new situations. In this respect, we propose implementation details of a knowledge taxonomy and an augmented data mining life cycle.