Learning by Constructing Collaborative Representations: An Empirical Comparison of Three Alternatives.

: Given the explosive growth in the use of computer media for learning and the wide range of choices available to designers of online learning tools, it is crucial to understand how these design choices may influence learning. This study evaluated the influence of tools for constructing representations of evidential models on collaborative learning processes and outcomes. Pairs of participants worked with one of three representations while investigating complex science and public health problems. Dependent variables included quantity of discourse about evidential relations ("for" and "against") and two learning outcome measures. Significant effects of tools on learning processes were found, although there appears to have been insufficient time for these process differences to influence learning outcomes. Keywords: collaborative representations, representational guidance 1. Introduction The importance of social processes to learning, including the potential utility of collaborative l..

The Effects of Representation on Students' Elaborations in Collaborative Inquiry.

In order to better understand how software design choices may influence students ’ collaborative learning, we conducted a study of the influence of tools for constructing representations of evidential models on collaborative learning processes and outcomes. Pairs of participants worked with one of three representations (matrix, graph, text) while investigating a complex public health problem. Focusing on students ’ collaborative investigative processes and post-hoc essays, we present several analyses that assess the impact of representation type on students ’ elaborations of their emerging knowledge. Our analyses indicate significant impacts on the extent to which students revisit knowledge and the likelihood that they will use that knowledge later

Toward the Development of Highly Interactive Software Visualization Systems: A User-Centered Approach

Introduction Predicated on the intuitive idea that a mapping between an executing program and computer graphics can give one insight into the programs dynamic behavior, software visualization (SV) systems purport to provide techniques for facilitating four central activities involving that mapping: its (1) design, (2) specification, (3) observation, and (4) manipulation. Early SV systems such as Balsa (Brown 1987) and Tango (Stasko 1990) defined conceptual models in which different actors performed those activities.. In the Balsa model, for example, client programmers--- algorithmaticians and animators---were responsible for designing and specifying the algorithm-to-graphics mapping, whereas the end users---script authors and script viewers---actually observed the mappings (see Figure 1). Although, in theory, the same person could have assumed all four roles, in practice they were