A case-based, small-group cooperative learning course in preclinical

Moving beyond the general question of effectiveness of small group learning, this conceptual review proposes conditions under which the use of small groups in classrooms can be productive. Included in the review is recent research that manipulates various features of cooperative learning as well

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric

in several respects: (1) markets tolerate inefficient firms, so cross-firm productivity dispersion is high; (2) small groups of entrenched oligopolists exploit monopoly power in product markets; and (3) many small firms are unable or unwilling to grow, so important scale economies go unexploited

of the fruits of this approach, we discuss the concept of `articulation work' and its relevance to CSCW. This raises a host of interesting problems that are marginalized in the work on small group support but critical to the success of CSCW systems `in the large', i. e., that are designed to meet

of a recta-analysis that integrates research on undergraduate SMET education since 1980. The meta-analysis dem-onstrates that various forms of small-group learning are effective in promoting greater academic achievement, more favorable attitudes toward learning, and increased persistence through SMET

This study compared the effects on achievement and verbal interaction of two instructional programs designed to teach students how to work effectively in small groups: cooperative learning with instruction and practice in basic com-munication skills and academic helping skills (experimental

two of the fundamental problems in unsupervised learning (Duda & Hart, 1973; Chapter 6). In clustering, the goal is to group data points by similarity between their features. Conversely, in dimensionality reduction, the goal is to group (or compress) features that are highly correlated

-interval splits. 1. Introduction Most empirical learning systems are given a set of pre-classified cases, each described by a vector of attribute values, and construct from them a mapping from attribute values to classes. The attributes used to describe cases can be grouped into continuous attributes, whose

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2