Modeling with a Conceptual representation: is it necessary? does it Work?

In response to recent educational imperatives in the United States, modeling and systems thinking have been identified as being critical for science learning. In this paper, we investigate models in the classroom from two important perspectives: (1) from the teacher perspective to understand how teachers perceive models and use models in the classroom and (2) from the students perspective to understand how student use model-based reasoning to represent their understanding in a classroom setting. Qualitative data collected from 19 teachers who attended a professional development workshop in the northeastern United States indicate that while teachers see the value in teaching to think with models (i.e., during inquiry practices), they tend to use models mostly as communication tools in the classroom. Quantitative data collected about the modeling practices of 42 middle school students who worked collaboratively in small groups (4–5 students) using a computer modeling program indicated that students tended to engage in more mechanistic and function-related thinking with time as they reasoned about a complex system. Furthermore, students had a typified trajectory of first adding and then next paring down ideas in their models. Implications for science education are discussed

Modeling with a Conceptual representation: is it necessary? does it Work?

In response to recent educational imperatives in the United States, modeling and systems thinking have been identified as being critical for science learning. In this paper, we investigate models in the classroom from two important perspectives: (1) from the teacher perspective to understand how teachers perceive models and use models in the classroom and (2) from the students perspective to understand how student use model-based reasoning to represent their understanding in a classroom setting. Qualitative data collected from 19 teachers who attended a professional development workshop in the northeastern United States indicate that while teachers see the value in teaching to think with models (i.e., during inquiry practices), they tend to use models mostly as communication tools in the classroom. Quantitative data collected about the modeling practices of 42 middle school students who worked collaboratively in small groups (4–5 students) using a computer modeling program indicated that students tended to engage in more mechanistic and function-related thinking with time as they reasoned about a complex system. Furthermore, students had a typified trajectory of first adding and then next paring down ideas in their models. Implications for science education are discussed

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons

Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach

Despite recent molecular technique improvements, biological knowledge remains incomplete. Reasoning on living systems hence implies to integrate heterogeneous and partial informations. Although current investigations successfully focus on qualitative behaviors of macromolecular networks, others approaches show partial quantitative informations like protein concentration variations over times. We consider that both informations, qualitative and quantitative, have to be combined into a modeling method to provide a better understanding of the biological system. We propose here such a method using a probabilistic-like approach. After its exhaustive description, we illustrate its advantages by modeling the carbon starvation response in Escherichia coli. In this purpose, we build an original qualitative model based on available observations. After the formal verification of its qualitative properties, the probabilistic model shows quantitative results corresponding to biological expectations which confirm the interest of our probabilistic approach.Comment: 10 page

Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective

Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base [Console&Torasso91], and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form [Poole94]. Since in Model-Based Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be modeled, this paper discusses the relation between modeling, and in particular abstraction in the model, and the notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200