In case-based reasoning, a given problem is solved by adapting the solutions to similar problems encountered in the past. A major task in case-based problem solving is to generate modifications that are useful for adapting a previous solution to solve the present problem. We describe a model-based method that uses qualitative models of cjises for generating useful modifications. The qualitative model of a case expresses a problem solver's comprehension of how the solution satisfies the constraints of the problem. We illustrate the model based method in the context of case-based design of physical devices. A designer's understanding of how the structure of a previously encountered design produces its functions is expressed in the form of a function structure model. The functional differences between a given problem and a specific case are mapped into structural modifications by a family of modification generation plans. Each plan is applicable to a specific type of functional difference, and uses the function structure model to identify the specific components that need to be modified. We discuss the evaluation of this model-based method in

Massive Open Online Courses (MOOCs) once offered the promise of accessibility and affordability. However, MOOCstypically lack expert feedback and social interaction, and have low student engagement and retention. Thus, alternativeprograms for online education have emerged including an online graduate program in computer science at a major publicuniversity in USA. This program is considered a success with over 9000 students now enrolled in the program. We adoptthe perspective of cognitive science to answer the question why do only some online educational courses succeed? Wemeasure learner motivation and self-regulation in one course in the program, specifically a course on artificial intelligence(AI). Surveys of students indicate that students self-reported assessments of self-efficacy, cognitive strategy use, andintrinsic value of the course are not only fairly high, but also generally increase over the course of learning. This datasuggests that the online AI course might be a success because the students have high self-efficacy and the class fostersself-regulated learning.

In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of caseadaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. W e describe a modelbased method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate G T M . The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation.

Metacognitive skills are critical in learning but difficult to teach. Thus the question becomes how can we facilitate metacognitive tutoring? We present an exploratory learning environment called MILA-T with embedded metacognitive tutors imitating five functional roles of teachers in classrooms. We tested MILA–T in a controlled experiment with 237 middle school students. We examine the impact of MILA–T on the models of a natural phenomenon constructed by the students. We find that students with access to MILA–T wrote better evidential justifications for their models, and thus, deliver better-justified models for the phenomenon. We also find that these improvements persisted during a transfer task. These results lend support for organizing metacognitive tutoring around the functional roles of teachers for supporting inquiry-driven modeling

Virtual laboratories that enable novice scientists to construct, evaluate and revise models of complex systems heavily involve parameter estimation tasks. We seek to understand novice strategies for parameter estimation in model exploration to design better cognitive supports for them. We conducted a study of 50 college students for a parameter estimation task in exploring an ecological model. We identified three types of behavioral patterns and their underlying cognitive strategies. Specifically, the students used systematic search, problem decomposition and reduction, and global search followed by local search as their cognitive strategies.

Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene’s odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene’s test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene’s test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry.