Semantic and Qualitative Physics-Based Reasoning on Plain-English Flow Terms for Generating Function Model Alternatives

In graph-based function models, the function verbs and flow nouns are usually chosen from predefined vocabularies. The vocabulary class definitions, combined with function modeling grammars defined at various levels of formalism, enable function-based reasoning. However, the text written in plain English for the names of the functions and flows is presently not exploited for formal reasoning. This paper presents a formalism (representation and reasoning) to support semantic and physics-based reasoning on the information hidden in the plain-English flow terms, especially for automatically decomposing black box function models, and to generate multiple design alternatives. First, semantic reasoning infers the changes of flow types, flow attributes, and the direction of those changes between the input and output flows attached to the black box. Then, a representation of qualitative physics is used to determine the material and energy exchanges between the flows and the function features needed to achieve them. Finally, a topological reasoning is used to infer multiple options of composing those function features into topologies and to thus generate multiple alternative decompositions of the functional black box. The data representation formalizes flow phases, flow attributes, qualitative value scales for the attributes, and qualitative physics laws. An eight-step algorithm manipulates these data for reasoning. This paper shows four validation case studies to demonstrate the workings of this formalism.

Students’ problem-solving strategies in qualitative physics questions in a simulation-based formative assessment

Previous studies on quantitative physics problem solving have been concerned with students’ using equations simply as a numerical computational tool. The current study started from a research question: “How do students solve conceptual physics questions in simulation-based formative assessments?” In the study, three first-year college students’ interview data were analyzed to characterize their problem-solving strategies in qualitative physics questions. Prior to the interview, the participating students completed four formative assessment tasks in physics integrating computer simulations and questions. The formative assessment questions were either constructed-response or two-tiered questions related to the simulations. When interviewing students, they were given two or three questions from each task and asked to think aloud about the questions. The findings showed that students still used equations to answer the qualitative questions, but the ways of using equations differed between students. The study found that when students were able to connect variables to a physical process and to interpret relationships among variables in an equation, equations were used as explanatory or conceptual understanding tools, not just as computational tools.

Problem-Solving Approach in Multiple Representations of Qualitative and Quantitative Problems in Kinematics Motion

The study aims to find the approach used by students in solving physics problems with symbol and graph representations and also to find out the student's approach to solving qualitative and quantitative questions. This is related to student good problem solvers use multiple representations to solve the problem. They use the qualitative and quantitative approach in a physics problem. The type of research conducted to find out this is quantitative descriptive. In this study, data were obtained by tests and interviews. We give four problems in kinematic motions to undergraduate students. The problem consists of a graphic and symbol representation of qualitative and quantitative problems. The result shows that the quantitative problem of symbol and graphic representation, all of the students only solve the problem without qualitative analysis. That proves in these problems all of the students become a novice problem solver in this problem. Different from it, 84% of students not only solve the qualitative physics problem in symbol representation problems with the qualitative approach but also, we solve with quantitative analysis. On the other hand, they only explain the graph representations problem with descriptions