Smith, Thompson, and Mountcastle on the Boltzmann Factor

Trevor I. Smith, John R. Thompson, and Donald B. Mountcastle

Addressing Student Difficulties with Statistical Mechanics: The Boltzmann Factor

AIP Conf. Proc. -- October 24, 2010 -- Volume 1289, pp. 305-308

As part of research into student understanding of topics related to thermodynamics and statistical mechanics at the upper division, we have identified student difficulties in applying concepts related to the Boltzmann factor and the canonical partition function. With this in mind, we have developed a guided-inquiry worksheet activity (tutorial) designed to help students develop a better understanding of where the Boltzmann factor comes from and why it is useful. The tutorial guides students through the derivation of both the Boltzmann factor and the canonical partition function. Preliminary results suggest that students who participated in the tutorial had a higher success rate on assessment items than students who had only received lecture instruction on the topic. We present results that motivate the need for this tutorial, the outline of the derivation used, and results from implementations of the tutorial. ©2010 American Institute of Physics

Hawkins et al. on vector addition

Jeffrey M. Hawkins, John R. Thompson, Michael C. Wittmann, Eleanor C. Sayre, and Brian W. Frank

AIP Conf. Proc. -- October 24, 2010 -- Volume 1289, pp. 165-168

We investigate if the visual representation of vectors can affect which methods students use to add them. We gave students one of four questions with different graphical representations, asking students to add the same two vectors. For students in an algebra-based class the arrangement of the vectors had a statistically significant effect on the vector addition method chosen while the addition or removal of a grid did not. ©2010 American Institute of Physics


Springuel Ph.D.: Cluster analysis in kinematics and the FMCE

Applying Cluster Analysis to Physics Education Research Data
R. Padraic Springuel, 2010

One major thrust of Physics Education Research (PER) is the identification of student ideas about specific physics concepts, both correct ideas and those that differ from the expert consensus. Typically the research process of eliciting the spectrum of student ideas involves the administration of specially designed questions to students. One major analysis task in PER is the sorting of these student responses into thematically coherent groups. This process is one which has previously been done by eye in PER. This thesis explores the possibility of using cluster analysis to perform the task in a more rigorous and less time-intensive fashion while making fewer assumptions about what the students are doing. Since this technique has not previously been used in PER, a summary of the various kinds of cluster analysis is included as well as a discussion of which might be appropriate for the task of sorting student responses into groups. Two example data sets (one based on the Force and Motion Conceptual Evaluation (FMCE) the other looking at acceleration in two-dimensions (A2D) are examined in depth to demonstrate how cluster analysis can be applied to PER data and the various considerations which must be taken into account when doing so. In both cases, the techniques described in this thesis found 5 groups which contained about 90% of the students in the data set. The results of this application are compared to previous research on the topics covered by the two examples to demonstrate that cluster analysis can effectively uncover the same patterns in student responses that have already been identified.

Recommended Citation
Springuel, R. Padraic, "Applying Cluster Analysis to Physics Education Research Data" (2010). Electronic Theses and Dissertations. 1086.