In this paper, we suggest that case-based resources, which are used for assisting cognition during problem solving, can be structured around the work of narratives in social cultural psychology. Theories and other research methods have proposed structures within narratives and stories which may be useful to the design of case-based resources. Moreover, embedded within cases are stories which are contextually rich, supporting the epistemological groundings of situated cognition. Therefore the purposes of this paper are to discuss possible frameworks of case-stories; derive design principles as to “what ” constitutes a good case story or narrative; and suggest how technology can support story-based learning. We adopt video-based Computer-Supported Collaborative Learning (CSCL) technology to support problem solving with casestories learning scenarios. Our hypothesis in this paper is that well-designed case-based resources are able to aid in the cognitive processes undergirding problem solving and meaning making. We also suggest the use of an emerging video-based collaborative learning technology to support such an instructional strategy.

ABSTRACT. Moral text processing was used as an ecologically valid method for assessing implicit and explicit moral understanding and development. The authors tested undergraduates, seminarians, and graduate students in political science and philosophy for recall of moral narratives and moral expository texts. Multivariate analyses of covariance using educational experience as an independent variable, age and moral judgment score as covariates, and recall of embedded moral arguments as dependent variables revealed a relation between education and level of moral arguments recalled. Lower-stage moral reasoning was best recalled by undergraduates, whereas higher-stage reasoning was best recalled by graduate students, with seminarians intermediate for both types of text. Moral judgment score was related to recall of the highest-level moral arguments even when age and educational experience were controlled. Moral judgment development appeared to be particularly helpful in recall of expository compared with narrative texts.

The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognitionbased moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli,