Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

This paper introduces a model for design exploration based on notions of evolution and demonstrates computational co-evolution using a modified genetic algorithm (GA). Evolution is extended to consider co-evolution where two systems evolve in response to each other. Co-evolution in design exploration supports the change, over time, of the design solution and the design requirements. The basic GA, which does not support our exploration model, evaluates individuals from a population of design solutions with an unchanged fitness function. This approach to evaluation implements search with a prefixed goal. Modifications to the basic GA, are required to support exploration. Two approaches to implement a co-evolving GA are: a combined gene approach and a separate spaces approach. The combined gene approach includes the representation of the requirements and the solution within the genotype. The separate spaces approach models the requirements and the solutions as separately evolving interacting populations of genotypes. The combined gene approach is developed further in this paper and used to demonstrate design exploration in the domain of braced frame design for buildings. The issues related to the coding of the genotype, mapping to a phenotype, and evaluation of the phenotype are addressed. Preliminary results of co-evolution are presented that show how exploration differs from search.

Abstract. Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space. The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives. Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification. This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions. The study shows that co-evolutionary design is a good cognitive model of design, and highlights the similarities and differences between the computational model and the cognitive model. The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process.Keywords: co-evolutionary design, computational model of design, cognitive model of design, protocol studies

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Most computer‐based design tools assume that designers work with a well‐defined problem. However, this assumption has been challenged by current research. The explorative aspect of design, especially during conceptual design, is not fully addressed. This paper introduces a model for problem‐design exploration and how this model can be implemented using the genetic algorithm (GA) paradigm. The basic GA, which does not support our exploration model, evaluates individuals from a population of design solutions with an unchanged fitness function. This approach to evaluation implements search with a prefixed goal. Modifications to the basic GA are required to support exploration. Two approaches to implement a co‐evolving GA are presented and discussed in this paper: one in which the fitness function is represented within the genotype, and a second in which the fitness function is modeled as a separately evolving population of genotypes.

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The basic principles of a flipped classroom teaching method are to deliver content outside of the class and to move active learning into the classroom. There are many strategies for delivering the content online, such as having instructors prepare online lectures, wrapping the course around a MOOC, and curating online videos from various sources. There are also many strategies for including active learning in the classroom that go beyond providing programming labs, and can include various forms of peer instruction. In this paper we describe our experiences flipping four different computer science classes across multiple semesters over two years. This breadth of experience with classroom flipping has enabled us to compare strategies and approaches and develop an understanding of which approaches appear to work under which circumstances. We discuss how we structured out-of-class preparatory work, how we created or sourced online videos, how we used active learning activities in-class to scaffold skills development and identify students' misconceptions, and how we structured teams for in class activities. This paper contributes a set of flexible strategies to consider for provision of curricular content out-of-class, structuring students' preparatory work, applying active learning of skills and concepts, and leveraging social interaction and peer instruction for CS education. We present the impact of our approaches based upon leading indicators of course evaluations and student surveys. We discuss lessons learned and students' responses to our strategies.

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