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The paper presents the initial results of a comparative Polish-Japanese study. The research was conducted at the Department of Mathematics, Physics and Technical Science at the Pedagogical University of Cracow and at the University of Hokkaido. The participants of the study were university students of humanistic courses. The research concerns the comparison of the respondents’ knowledge and understanding of the concept of force in mechanics and their ways of solving problems in the field of a basic mechanics course. A special theoretical tool was used. It was the standardized, international test diagnosing the understanding of the concept of force - the so-called “Force Concept Inventory” (FCI), in its official Polish and Japanese translations. The eye-tracking method was combined with structured interviews and discussions with all the respondents.

The creation of the Force Concept Inventory (FCI) was a seminal moment for Physics Education Research. Based on the development of the FCI, many more concept inventories have been developed. The problem with the development of all of these concept inventories is there does not seem to be a concise methodology for developing these inventories, nor is there a concise definition of what these inventories measure. By comparing the development methodologies of many common Physics and Astronomy Concept Inventories we can draw inferences about different types of concept inventories, as well as different valid conclusions that can be drawn from the administration of these inventories. Inventories compared include: Astronomy Diagnostic Test (ADT), Brief Electricity and Magnetism Assessment (BEMA), Conceptual Survey in Electricity and Magnetism (CSEM), Diagnostic Exam Electricity and Magnetism (DEEM), Determining and Interpreting Resistive Electric Circuits Concept Test (DIRECT), Energy and Motion Conceptual Survey...

There is widespread concern about the level of scientific literacy in the U. S. An important, although often overlooked, point, is that student learning is generally only a good as the assessments used to measure it. Unfortunately, most assessments measure recall and recognition rather than conceptual understanding, and as a result over‐estimate levels of scientific literacy. We have encountered this fact during the construction of the Biology Concept Inventory (BCI). Using the concept of diffusion, which is taught in a wide range of introductory biology, chemistry, and physics courses, as an exemplar, we describe lessons learned and strategies we use to create questions that better probe student understanding.

To determine the overall effectiveness of instruction at producing a scientifically correct understanding, researchers need to be able to assess conceptual change. This paper details how using Model Analysis Theory (MAT) in conjunction with the Lunar Phases Concept Inventory (LPCI), provides researchers a more detailed picture of college students conceptual change with regards to lunar phases than traditional methods alone. A review of MAT is provided along with a detailed example of its application before and after instruction to determine conceptual change.

The Lunar Phases Concept Inventory (LPCI) is a twenty‐item multiple‐choice inventory developed to aid instructors in assessing the mental models their students utilize when answering questions concerning phases of the moon. Based upon an in‐depth qualitative investigation of students’ understanding of lunar phases, the LPCI was designed to take advantage of the innovative model analysis theory to probe the different dimensions of students’ mental models of lunar phases. As part of a national field test, pre‐instructional LPCI data was collected for over 750 students from multiple post‐secondary institutions across the United States and Canada. Application of model analysis theory to this data set allowed researchers to probe the different mental models of lunar phases students across the country utilize prior to instruction. Results of this analysis display strikingly similar results for the different institutions, suggesting a potential underlying cognitive framework.

By studying the patterns of a group of individuals’ responses to a series of multiple‐choice questions, researchers can utilize Model Analysis Theory to create a probability distribution of mental models for a student population. The eigenanalysis of this distribution yields information about what mental models the students possess, as well as how consistently they utilize said mental models. Although the theory considers the probabilistic distribution to be fundamental, there exists opportunities for random errors to occur. In this paper we will discuss a numerical approach for mathematically accounting for these random errors. As an example of this methodology, analysis of data obtained from the Lunar Phases Concept Inventory will be presented. Limitations and applicability of this numerical approach will be discussed.