Analytical Models for Minority Representation in Academic Departments

We present mathematical models for the evolution over time of the proportion of minorities in an academic department or similarly selected group of constant size. Using the models, we analyze both the steady-state and time-dependent behavior of the proportion of minorities, and also obtain a means of evaluating the effectiveness of a department's hiring history. We derive a number of surprising results with importance to institutional hiring policy and affirmative action; and we also present methods, suggested by the model and its behavior, to improve departmental hiring practices.     

Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific Phenomena

This article explores how learning biological concepts can be facilitated by playing a video game that depicts interactions and processes at the subcellular level. Particularly, this article reviews the effects of a real-time strategy game that requires players to control the behavior of a virus and interact with cell structures in a way that resembles the actual behavior of biological agents. The evaluation of the video game presented here aims at showing that video games have representational advantages that facilitate the construction of dynamic mental models. Ultimately, the article shows that when video game’s characteristics come in contact with expert knowledge during game design, the game becomes an excellent medium for supporting the learning of disciplinary content related to dynamic processes. In particular, results show that students who participated in a game-based intervention aimed at teaching biology described a higher number of temporal-dependent interactions as measured by the coding of verbal protocols and drawings than students who used texts and diagrams to learn the same topic.     

Electroencephalographic Coherence and Learning: Distinct Patterns of Change During Word Learning and Figure Learning Tasks

One likely mechanism in learning new skills is change in synchronous connections between distributed neural networks, which can be measured by coherence analysis of electroencephalographic patterns. This study examined coherence changes during the learning of two tasks, a word association task and a figure association task. Although learning curves were similar for both tasks, distinct patterns of coherence change were observed. Coherence tended to increase as learning progressed in the figure association task. In contrast, coherence tended to decrease in the word association task, especially within hemisphere. Word learning was coupled with negative intrahemispheric and positive interhemispheric performance–coherence relations in the gamma frequency. Unique to the figure learning task was an increase in the number of positive coherence–performance relations in both delta and theta frequencies across blocks. Results are discussed in light of ongoing efforts to identify the mechanisms that coordinate distributed brain activities during the process of learning. Further research is needed to define patterns of coherence change for different tasks, goals, and brain regions.