The introduction of the Force Concept Inventory (FCI) by produced a remarkable impact within the community of physics teachers. An instrument to measure student comprehension of the Newtonian concept of force, the FCI demonstrates that active learning leads to far superior student conceptual learning than didactic lectures. Compared to a working knowledge of physics, biological literacy and illiteracy have an even more direct, dramatic, and personal impact. They shape public research and reproductive health policies, the acceptance or rejection of technological advances, such as vaccinations, genetically modified foods and gene therapies, and, on the personal front, the reasoned evaluation of product claims and lifestyle choices. While many students take biology courses at both the secondary and the college levels, there is little in the way of reliable and valid assessment of the effectiveness of biological education. This lack has important consequences in terms of general bioliteracy and, in turn, for our society. Here we describe the beginning of a community effort to define what a bioliterate person needs to know and to develop, validate, and disseminate a tiered series of instruments collectively known as the Biology Concept Inventory (BCI), which accurately measures student comprehension of concepts in introductory, genetic, molecular, cell, and developmental biology. The BCI should serve as a lever for moving our current educational system in a direction that delivers a deeper conceptual understanding of the fundamental ideas upon which biology and biomedical sciences are based.

Rapid advancements in hardware, software, and connectivity are helping to shorten the times needed to develop computer simulations for science education. These advancements, however, have not been accompanied by corresponding theories of how best to design and use these technologies for teaching, learning, and testing. Such design frameworks ideally would be guided less by the strengths/limitations of the presentation media and more by cognitive analyses detailing the goals of the tasks, the needs and abilities of students, and the resulting decision outcomes needed by different audiences. This article describes a problem-solving environment and associated theoretical framework for investigating how students select and use strategies as they solve complex science problems. A framework is first described for designing on-line problem spaces that highlights issues of content, scale, cognitive complexity, and constraints. While this framework was originally designed for medical education, it has proven robust and has been successfully applied to learning environments from elementary school through medical school. Next, a similar framework is detailed for collecting student performance and progress data that can provide evidence of students' strategic thinking and that could potentially be used to accelerate student progress. Finally, experimental validation data are presented that link strategy selection and use with other metrics of scientific reasoning and student achievement.

A large-enrollment, undergraduate cellular biology lecture course is described whose primary goal is to help students acquire skill in the interpretation of experimental data. The premise is that this kind of analytical reasoning is not intuitive for most people and, in the absence of hands-on laboratory experience, will not readily develop unless instructional methods and examinations specifically designed to foster it are employed. Promoting scientific thinking forces changes in the roles of both teacher and student. We describe didactic strategies that include directed practice of data analysis in a workshop format, active learning through verbal and written communication, visualization of abstractions diagrammatically, and the use of ancillary small-group mentoring sessions with faculty. The implications for a teacher in reducing the breadth and depth of coverage, becoming coach instead of lecturer, and helping students to diagnose cognitive weaknesses are discussed. In order to determine the efficacy of these strategies, we have carefully monitored student performance and have demonstrated a large gain in a pre- and posttest comparison of scores on identical problems, improved test scores on several successive midterm examinations when the statistical analysis accounts for the relative difficulty of the problems, and higher scores in comparison to students in a control course whose objective was information transfer, not acquisition of reasoning skills. A novel analytical index (student mobility profile) is described that demonstrates that this improvement was not random, but a systematic outcome of the teaching/learning strategies employed. An assessment of attitudes showed that, in spite of finding it difficult, students endorse this approach to learning, but also favor curricular changes that would introduce an analytical emphasis earlier in their training.