A Faculty Professional Development Model That Improves Student Learning, Encourages Active-Learning Instructional Practices, and Works for Faculty at Multiple Institutions.

Helping faculty develop high-quality instruction that positively affects student learning can be complicated by time limitations, a lack of resources, and inexperience using student data to make iterative improvements. We describe a community of 16 faculty from five institutions who overcame these challenges and collaboratively designed, taught, iteratively revised, and published an instructional unit about the potential effect of mutations on DNA replication, transcription, and translation. The unit was taught to more than 2000 students in 18 courses, and student performance improved from preassessment to postassessment in every classroom. This increase occurred even though faculty varied in their instructional practices when they were teaching identical materials. We present information on how this faculty group was organized and facilitated, how members used student data to positively affect learning, and how they increased their use of active-learning instructional practices in the classroom as a result of participation. We also interviewed faculty to learn more about the most useful components of the process. We suggest that this professional development model can be used for geographically separated faculty who are interested in working together on a known conceptual difficulty to improve student learning and explore active-learning instructional practices.

What Motivates Biology Instructors to Engage and Persist in Teaching Professional Development?

We conducted a study of 19 biology instructors participating in small, local groups at six research-intensive universities connected to the Automated Analysis of Constructed Response (AACR) project (www.msu.edu/∼aacr). Our aim was to uncover participants' motivation to persist in a long-term teaching professional development effort, a topic that is understudied in discipline-based educational research. We interviewed each participant twice over a 2-year period and conducted qualitative analyses on the data, using expectancy-value theory as a framework for considering motivation. Our analyses revealed that motivation among instructors was high due to their enjoyment of the AACR groups. The high level of motivation is further explained by the fact that AACR groups facilitated instructor involvement with the larger AACR project. We also found that group dynamics encouraged persistence; instructors thought they might never talk with colleagues about teaching in the absence of AACR groups; and groups were perceived to have a low-enough time requirement to warrant sustained involvement. We conclude that instructors have persisted in AACR groups because the groups provided great value with limited cost. The characterization of instructor experiences described here can contribute to a better understanding of faculty needs in teaching professional development.

Applying computerized-scoring models of written biological explanations across courses and colleges: prospects and limitations.

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students' written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors' and nonmajors' written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of "near-perfect" agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations.

Assessing students' ability to trace matter in dynamic systems in cell biology.

College-level biology courses contain many complex processes that are often taught and learned as detailed narratives. These processes can be better understood by perceiving them as dynamic systems that are governed by common fundamental principles. Conservation of matter is such a principle, and thus tracing matter is an essential step in learning to reason about biological processes. We present here multiple-choice questions that measure students' ability and inclination to trace matter through photosynthesis and cellular respiration. Data associated with each question come from students in a large undergraduate biology course that was undergoing a shift in instructional strategy toward making fundamental principles (such as tracing matter) a central theme. We also present findings from interviews with students in the course. Our data indicate that 1) many students are not using tracing matter as a tool to reason about biological processes, 2) students have particular difficulties tracing matter between systems and have a persistent tendency to interconvert matter and energy, and 3) instructional changes seem to be effective in promoting application of the tracing matter principle. Using these items as diagnostic tools allows instructors to be proactive in addressing students' misconceptions and ineffective reasoning.