Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experience across learners. Although well studied in human learning, there has been less attention to cooperative transmission of data in machine learning, and we consequently lack strong formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We p...

This paper presents the preliminary results from an experiment investigating the efficacy of cooperative learning on individual students in an introduction to information systems class. Statistical analysis of the experimental data indicates that cooperative learning did not have a positive effect on individual student learning or the efficiency with which an individual student learns. These re...

This paper uses a theoretical model of cooperative learning to examine the survivability of communities of learners. A primary assumption in this article is that individualbased learning tends to use local information to increase an individual’s “fitness” while collaborative learning, based on the sharing of information, knowledge, and resources, increases group fitness. We frame our discussion...

A structure composed of local linear perceptrons for approximating global class discriminants is investigated. Such local linear models may be combined in a cooperative or competitive way. In the cooperative model, a weighted sum of the outputs of the local perceptrons is computed where the weight is a function of the distance between the input and the position of the local perceptron. In the c...

This study presents results from a field experiment investigating the efficacy of cooperative learning on individual students in an undergraduate introduction to information systems class. Statistical analysis of the data indicates that cooperative learning did not have a positive effect on individual student learning. This result is in contrast to effective individual learning outcomes associa...