Secondary Students' Dynamic Modeling Processes: Analyzing, Reasoning About, Synthesizing, and Testing Models of Stream Ecosystems

In this paper, we explore dynamic modeling as an opportunity for students to think about the science content they are learning. We examined the “Cognitive Strategies for Modeling” (CSMs) in which students engaged as they created dynamic models. We audio- and videotape-recorded eight pairs of ninth grade science students and analyzed their conversations and actions. In analyzing appropriate objects and factors for their model, some students merely enumerated potential factors whereas others engaged in rich, substantial, mindful analysis. In reasoning about their models, students discussed relationships in depth, concentrated only on the most important key relationships, or encountered difficulty distinguishing between causal and correlational relationships. In synthesizing working models, students mapped their model to aid visualization, focused on their goal, or talked about their model's appearance or form. Students attempted to articulate explanations for their relationships, but sometimes their explanations were shallow. In testing their models, some students tested thoroughly but only a few persisted in debugging their model's behavior so that it matched their expectations. In our conclusion we suggest that creating dynamic models has great potential for use in classrooms to engage students in thought about science content, particularly in those thinking strategies best fostered by dynamic modeling: analysis, relational reasoning, synthesis, testing and debugging, and making explanations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45184/1/10956_2004_Article_412477.pd

Investigating processes and products of secondary science students using dynamic modeling software.

With the development of learner-supporting software and the availability of powerful desktop computers in classrooms, dynamic modeling is becoming accessible to secondary science students. Dynamic modeling should engage learners in analyzing phenomena into parts, reasoning about relationships between them, synthesizing relationships into a model, and testing a model's fit with reality. Cognitive strategies fostered by dynamic modeling environments should allow students to go beyond fact retention to making connections between ideas and concepts they have learned and to developing deeper understandings of scientific phenomena. This dissertation qualitatively explores, in three parts, research questions related to processes and products of students creating dynamic models of stream ecosystems using learner-centered modeling software called "Model-It." The first part examines quality and characteristics of students' Cognitive Strategies for Modeling (analyzing, relational reasoning, synthesizing, and testing/debugging), or CSMs, and General Conceptual Strategies for Modeling (planning, explaining, searching, and questioning), or GCSMs. Eight case studies of pairs of secondary students demonstrated that students' CSMs and GCSMs varied in quality, but almost all groups were able to engage in some level of thoughtful CSMs, supported by the software and by instruction. The second part is an analysis of students' models as artifacts of understanding in terms of structure, scientific content, behavior, and craft. Examination of fifty models showed that students were able to construct dynamic models that were generally coherent, accurate, and sensibly behaved, relative to students' level of experience with science and with dynamic modeling. The third part addresses the question of whether students' modeling processes were related to what they produced, that is, whether thoughtful, effective strategies led to conceptually rich products. Results indicate that students who engaged in the full range of analyzing, relational reasoning, synthesizing, and testing/debugging created models that were coherent, accurate, and exhibited reasonable behavior with fairly high fidelity to the real world. This dissertation shows that dynamic modeling is a viable, promising classroom activity fostering engagement in Cognitive Strategies for Modeling. However, its results also indicate that more investigation is needed into instructional and software support for better quality modeling processes of causal reasoning, testing and debugging, and General Conceptual Strategies for Modeling.Ph.D.EducationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/105155/1/9635621.pdfDescription of 9635621.pdf : Restricted to UM users only

Pharmacological and biological evaluation of a series of substituted 1,4 naphthoquinone bioreductive drugs.

NoThe indolequinone compound EO9 has good pharmacodynamic properties in terms of bioreductive activation and selectivity for either NAD(P)H:quinone oxidoreductase-1 (NQO1)-rich aerobic or NQO1-deficient hypoxic cells. However, its pharmacokinetic properties are poor and this fact is believed to be a major reason for EO9's lack of clinical efficacy. The purpose of this study was to develop quinone-based bioreductive drugs that retained EO9's good properties, in terms of bioreductive activation, but have improved pharmacokinetic properties. Out of 11 naphthoquinone compounds evaluated, 2-aziridinyl-5-hydroxy-1,4-naphthoquinone (compound 2), 2,3-bis(aziridinyl)-5-hydroxy-1,4-naphthoquinone (compound 3), and 2-aziridinyl-6-hydroxymethyl-1,4-naphthoquinone (compound 11) were selected for further evaluation based on good substrate specificity for NQO1 and selectivity towards NQO1-rich cells in vitro. Compound 3 was of particular interest as it also demonstrated selectivity for NQO1-rich cells under hypoxic conditions. Compound 3 was not metabolised by murine whole blood in vitro (in contrast to compounds 2, 11 and EO9) and pharmacokinetic studies in non-tumour-bearing mice in vivo (at the maximum soluble dose of 60 mg kg¿1 administered intraperitoneally) demonstrated significant improvements in plasma half-life (16.2 min) and AUC values (22.5 ¿M h) compared to EO9 (T1/2 = 1.8 min, AUC = 0.184 ¿M h). Compound 3 also demonstrated significant anti-tumour activity against H460 and HCT-116 human tumour xenografts in vivo, whereas EO9 was inactive against these tumours. In conclusion, compound 3 is a promising lead compound that may target both aerobic and hypoxic fractions of NQO1-rich tumours and further studies to elucidate its mechanism of action and improve solubility are warranted