Virtual Collaboration in the Online Educational Setting: A Concept Analysis

This study was designed to explore the concept of virtual collaboration within the context of an online learning environment in an academic setting. Rodgers’ method of evolutionary concept analysis was used to provide a contextual view of the concept to identify attributes, antecedents, and consequences of virtual collaboration. Commonly used terms to describe virtual collaboration are collaborative and cooperative learning, group work, group interaction, group learning and teamwork. A constructivist pedagogy, group-based process with a shared purpose, support and web-based technology are required for virtual collaboration to take place. Consequences of virtual collaboration are higher order thinking and learning to work with others. A comprehensive definition of virtual collaboration is offered as an outcome of this analysis. Clarification of virtual collaboration prior to using it as a pedagogic tool in the online learning environment will enhance nursing education with the changes in nursing curriculum being implemented today. Further research is recommended to describe the developmental stages of the collaborative process among nursing students in online education and how virtual collaboration facilitates collaboration in practice

Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games

Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2×2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.Repeated Games, Evolution, Simulation

On crossing fitness valleys with the Baldwin Effect

Escaping local optima and crossing fitness valleys to reach higher-fitness regions of a fitness landscape is a ubiquitous concept in much writing on evolutionary difficulty. The Baldwin effect, an interaction between non-heritable lifetime plasticity (e.g. learning) and evolution, has been shown to be able to guide evolutionary change and ‘smooth out’ abrupt fitness changes in fitness landscapes –thus enabling genetic evolution that would otherwise not occur. However, prior work has not provided a detailed study or analysis on the saddle-crossing ability of the Baldwin effect in a simple multi-peaked landscape. Here we provide analytic and simulation studies to investigate the effectiveness and limitations of the Baldwin effect in enabling genotypic evolution to cross fitness valleys. We also discuss how canalisation, an aspect of many prior models of the Baldwin effect, is unnecessary for the Baldwin effect and a hindrance to its valley-crossing ability

A Model of the Use of Evolutionary Trees (MUET) to Inform K-14 Biology Education

Evolutionary trees are powerful tools used in modern biological research, and also commonly used in textbooks and classroom instruction. Studies have shown that K-14 students have difficulties interpreting evolutionary trees. To improve student learning about this topic, it is essential to teach them how to understand and use trees like professional biologists. Unfortunately, few currently used teaching frameworks for evolution instruction are designed for this purpose. In this study we developed the Model of the Use of Evolutionary Trees (MUET), a conceptual model that characterizes how evolutionary trees were used by professional biologists as represented in their research publications. The development of the MUET was guided by the Concept-Reasoning-Mode of representation (CRM) model as well as a “model of modeling” framework. The MUET was then used to review instructional and assessment material for K-14 classrooms. Future studies with the MUET may inform the development of teaching materials for K-14 classrooms aimed at improving students’ understanding of and learning about evolutionary trees