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Register a new userStudent Understanding of Taylor Series Expansions in Statistical Mechanics
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One goal of physics instruction is to have students learn to make physical meaning of specific mathematical ideas, concepts, and procedures in different physical settings. As part of research investigating student learning in statistical physics, we are developing curriculum materials that guide students through a derivation of the Boltzmann factor, using a Taylor series expansion of entropy. Using results from written surveys, classroom observations, and both individual think-aloud and teaching interviews, we present evidence that many students can recognize and interpret series expansions, but they often lack fluency with the Taylor series despite previous exposures in both calculus and physics courses. We present students successes and failures both using and interpreting Taylor series expansions in a variety of contexts.
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We present results of our investigation into student understanding of the physical significance and utility of the Boltzmann factor in several simple models. We identify various justifications, both correct and incorrect, that students use when answering written questions that require application of the Boltzmann factor. Results from written data as well as teaching interviews suggest that many students can neither recognize situations in which the Boltzmann factor is applicable, nor articulate the physical significance of the Boltzmann factor as an expression for multiplicity, a fundamental quantity of statistical mechanics. The specific student difficulties seen in the written data led us to develop a guided-inquiry tutorial activity, centered around the derivation of the Boltzmann factor, for use in undergraduate statistical mechanics courses. We report on the development process of our tutorial, including data from teaching interviews and classroom observations on student discussions about the Boltzmann factor and its derivation during the tutorial development process. This additional information informed modifications that improved students abilities to complete the tutorial during the allowed class time without sacrificing the effectiveness as we have measured it. These data also show an increase in students appreciation of the origin and significance of the Boltzmann factor during the student discussions. Our findings provide evidence that working in groups to better understand the physical origins of the canonical probability distribution helps students gain a better understanding of when the Boltzmann factor is applicable and how to use it appropriately in answering relevant questions.
Common research tasks ask students to identify a correct answer and justify their answer choice. We propose expanding the array of research tasks to access different knowledge that students might have. By asking students to discuss answers they may not have chosen naturally, we can investigate students abilities to explain something that is already established or to disprove an incorrect response. The results of these research tasks also provide us with information about how students responses vary across the different tasks. We discuss three underused question types, their possible benefits and some preliminary results from an electric circuits pretest utilizing these new question types. We find that the answer students most commonly choose as correct is the same choice most commonly eliminated as incorrect. Also, students given the correct answer can provide valuable reasoning to explain it, but they do not spontaneously identify it as the correct answer.
In the Fall of 2013, Georgia Tech offered a flipped calculus-based introductory mechanics class as an alternative to the traditional large-enrollment lecture class. This class flipped instruction by introducing new material outside of the classroom through pre-recorded, lecture videos. Video lectures constituted students initial introduction to course material. We analyze how students engaged with online lecture videos via clickstream data, consisting of time-stamped interactions (plays, pauses, seeks, etc.) with the online video player. Analysis of these events has shown that students may be focusing on elements of the video that facilitate a correct solution.
Prior research has shown that physics students often think about experimental procedures and data analysis very differently from experts. One key framework for analyzing student thinking has found that student thinking is more point-like, putting emphasis on the results of a single experimental trial, whereas set-like thinking relies on the results of many trials. Recent work, however, has found that students rarely fall into one of these two extremes, which may be a limitation of how student thinking is evaluated. Measurements of student thinking have focused on probing students procedural knowledge by asking them, for example, what steps they might take next in an experiment. Two common refrains are to collect more data, or to improve the experiment and collect better data. In both of these cases, the underlying reasons behind student responses could be based in point-like or set-like thinking. In this study we use individual student interviews to investigate how advanced physics students believe the collection of more and better data will affect the results of a classical and a quantum mechanical experiment. The results inform future frameworks and assessments for characterizing students thinking between the extremes of point and set reasoning in both classical and quantum regimes.
The basic notions of statistical mechanics (microstates, multiplicities) are quite simple, but understanding how the second law arises from these ideas requires working with cumbersomely large numbers. To avoid getting bogged down in mathematics, one can compute multiplicities numerically for a simple model system such as an Einstein solid -- a collection of identical quantum harmonic oscillators. A computer spreadsheet program or comparable software can compute the required combinatoric functions for systems containing a few hundred oscillators and units of energy. When two such systems can exchange energy, one immediately sees that some configurations are overwhelmingly more probable than others. Graphs of entropy vs. energy for the two systems can be used to motivate the theoretical definition of temperature, $T= (partial S/partial U)^{-1}$, thus bridging the gap between the classical and statistical approaches to entropy. Further spreadsheet exercises can be used to compute the heat capacity of an Einstein solid, study the Boltzmann distribution, and explore the properties of a two-state paramagnetic system.
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