Hampshire College

About: Hampshire College is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Genetic programming & Population. The organization has 461 authors who have published 998 publications receiving 40827 citations.

Student reactions to classroom lecture capture

Abstract: This paper evaluates the benefits and drawbacks of lecture recording, which aspects of lectures and lecture capture systems are most used, and what additional features and functions would make the experience more effective. We evaluated 4 computer science courses recorded during spring 2011 using our comprehensive lecture capture system PAOL and presented with webMANIC. We discuss the results of student surveys and focus groups and compare these with prior surveys that investigated how students reacted to the availability of online lecture content and how they used these resources in large- and small-scale deployments with both home-grown and commercial lecture capture technologies. The primary motivation for this study was to analyze how lecture capture fits in the context of computer science curricula and pedagogy and about how we can enhance our systems to be more educationally effective.

Genetic Programming with Epigenetic Local Search

Abstract: We focus on improving genetic programming through local search of the space of program structures using an inheritable epigenetic layer that specifies active and inactive genes. We explore several genetic programming implementations that represent the different properties that epigenetics can provide, such as passive structure, phenotypic plasticity, and inheritable gene regulation. We apply these implementations to several symbolic regression and program synthesis problems. For the symbolic regression problems, the results indicate that epigenetic local search consistently improves genetic programming by producing smaller solution programs with better fitness. Furthermore, we find that incorporating epigenetic modification as a mutation step in program synthesis problems can improve the ability of genetic programming to find exact solutions. By analyzing population homology we show that the epigenetic implementations maintain diversity in silenced portions of programs which may provide protection from premature convergence.

Kennel Enrichment: Exercise and Socialization of Dogs

Abstract: In the last 50 years, there has been a growing need for storage and management systems for the production and maintenance of large numbers of dogs. Unwanted dogs and strays, detained in kennels, stay for various lengths of time. Large kennels also produce dogs for sale as companion animals, for the service dog industry (police and guide dogs), for biomedical research, and for use by dog food companies. Across the United States, literally tens of thousands of dogs are born in kennels and spend their lives in kennels. The laboratory dog, the kennel dog, the service dog, and the companion dog are in an evolutionary transition period, accompanied by concomitant adaptation to stresses signaled by a high frequency of genetic disease and behavioral abnormalities. For kennel enrichment programs, such as socialization and exercise, the modern kenneled dog is a genetically moving target. Specific recommendations apply neither to all breeds nor to the variations within a single breed.

A General Platform for Inquiry Learning

Abstract: This research explores the use of software to move higher education towards more active student learning including inquiry, problem-based and cooperative learning. Active students ask their own questions, engage in hypothesis generation, make and test predictions about theories, are involved in reasoning and address their own misunderstandings. These activities place heavy demands on the faculty and are rarely allowed in large classrooms. The challenge is to support this type of inquiry learning without also adding and excessive burden to faculty time.We have built and evaluated software that supports inquiry learning in classrooms. The software supports students to reason about a phenomenon, make mistakes and monitor their own scientific processes. Intelligent tutoring and a discovery approach guide students' inquiry in problem-cases. The software provides some of the efficiency needed to make inquiry-oriented instruction more widely available and enhance problem-solving activities during classtime.We are expanding this model for inquiry-based learning across three domains, several institutions and teaching style. Student performance will be measured, providing empirical evidence for the portability of inquiry-oriented instruction for college-level classrooms. We will evaluate the comparative results concerning the differential effects of these interventions in each environment.

An approach to human–machine collaboration in innovation

Abstract: If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.