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This paper documents a case of model-building in biology through microanalysis of one student's interaction with 'Science for Living: The Circulatory System' (SFL), an interactive multimedia resource prototype for research. When SFL was used in a technology-rich, tenth grade (15-16 years of age) biology classroom, extensive data ranging from preconceptions tests to videotapes, projects and computer logs of work sessions were collected. This paper describes the student's learning goals, gains and activities with particular attention to interactions with representations, then contrasts them with those of classmates. The representations accessed by the learner were analysed with respect to contributions to mental models, the challenges posed by the representations and the effectiveness of the interface in overcoming those challenges. This rich and systematic description of model-building with multimedia enabled the formation of an initial model of model-based learning in this context.

This article reports on the collaboration of six states to study how simulation‐based science assessments can become transformative components of multi‐level, balanced state science assessment systems. The project studied the psychometric quality, feasibility, and utility of simulation‐based science assessments designed to serve formative purposes during a unit and to provide summative evidence of end‐of‐unit proficiencies. The frameworks of evidence‐centered assessment design and model‐based learning shaped the specifications for the assessments. The simulations provided the three most common forms of accommodations in state testing programs: audio recording of text, screen magnification, and support for extended time. The SimScientists program at WestEd developed simulation‐based, curriculum‐embedded, and unit benchmark assessments for two middle school topics, Ecosystems and Force & Motion. These were field‐tested in three states. Data included student characteristics, responses to the assessments, cognitive labs, classroom observations, and teacher surveys and interviews. UCLA CRESST conducted an evaluation of the implementation. Feasibility and utility were examined in classroom observations, teacher surveys and interviews, and by the six‐state Design Panel. Technical quality data included AAAS reviews of the items' alignment with standards and quality of the science, cognitive labs, and assessment data. Student data were analyzed using multidimensional Item Response Theory (IRT) methods. IRT analyses demonstrated the high psychometric quality (reliability and validity) of the assessments and their discrimination between content knowledge and inquiry practices. Students performed better on the interactive, simulation‐based assessments than on the static, conventional items in the posttest. Importantly, gaps between performance of the general population and English language learners and students with disabilities were considerably smaller on the simulation‐based assessments than on the posttests. The Design Panel participated in development of two models for integrating science simulations into a balanced state science assessment system. © 2012 Wiley Periodicals, Inc. J Res Sci Teach 49: 363–393, 2012

This research addresses high school students' understandings of the nature of models, and their interaction with model-based software in three science domains, namely, biology, physics, and chemistry. Data from 736 high school students' understandings of models were collected using the Students' Understanding of Models in Science (SUMS) survey as part of a large-scale, longitudinal study in the context of technology-based curricular units in each of the three science domains. The results of ANOVA and regression analyses showed that there were differences in students' pre-test understandings of models across the three domains, and that higher post-test scores were associated with having engaged in a greater number of curricular activities, but only in the chemistry domain. The analyses also showed that the relationships between the pre-test understanding of models subscales scores and post-test content knowledge varied across domains. Some implications are discussed with regard to how students' understanding of the nature of models can be promoted.

This paper describes part of a project called Modeling Across the Curriculum which is a largescale research study in 15 schools across the United States. The specific data presented and discussed here in this paper is based on BioLogica, a hypermodel, interactive environment for learning genetics, which was implemented in multiple classes in eight high schools. BioLogica activities, data logging, and assessments were refined across this series of implementations. All students took a genetics content knowledge pre-and posttests. Traces of students' actions and responses to computer-based tasks were electronically collected (via a "log file" function) and systematically analyzed. An intensive 3-day field test involving 24 middle school students served to refine methods and create narrative profiles of students' learning experiences, outcomes, and interactions with BioLogica. We report on one high school implementation and the field test as self-contained studies to document the changes and the outcomes at different phases of development. A discussion of design changes concludes this paper.KEY WORDS: genetics; model-based learning; interactive environments; data logging; technologyenhanced assessment.With support from NSF, the Concord Consortium developed an interactive, computer-based learning environment, BioLogica, that is designed to support students in high school classrooms to build a deep understanding of core concepts in Mendelian genetics. The pedagogical challenges are numerous. What do we mean by deep understanding? How can we help them develop deep understanding? How do we know when they've done so? This paper focuses on the learning that takes place when students use BioLogica, an interactive genetics curriculum, in their high 1 The Concord Consortium, 10 Concord Crossing, Suite 300, Concord, Massachusetts 01742. 2 Educational Designs Unlimited, Inc., Hillsborough, New Jersey. 3 School of Eduation and Social Policy, Northwestern University, Chicago, Illinois. 4 Graduate School of Education, Harvard University, Cambridge, Massachusetts. 5 To whom correspondence should be addressed; e-mail: bbuckley@concord.org school classrooms. It presents the model of learning we use, what this looks like in practice, how we determine the nature and extent of student learning when BioLogica is used in high school classrooms, and what we've learned about all of the above. MODEL-BASED LEARNINGThe theory we employ is an elaboration and extension of Model-Based Teaching and Learning (MBTL) set forth in a special issue of the International Journal of Science Education (Gobert and Buckley, 2000). The tenets of model-based learning are based on the presupposition that understanding requires the construction of mental models of the phenomena under study, and that all subsequent problem-solving, inferencing, or reasoning are done by means of manipulating or "running" these mental models (Johnson-Laird, 1983). We view mental models as internal, cognitive representations used in Buckley, Gobert, Kindfield, Horwitz, Tinker, ...