Rasch Analysis in Physics Education Research: Why Measurement Matters

The Rasch model is a probabilistic model which describes the interaction of persons (test takers/survey respondents) with test/survey items and is governed by two parameters: item difficulty and person ability. Rasch measurement parallels physical measurement processes by constructing and using linear person and item measures that are independent of the particular characteristics of the sample and the test items along a unidimensional construct. The model’s properties make it especially suitable for test construction and evaluation as well as the development and use of surveys. The evaluation of item fit with the model can pinpoint problematic items and flag idiosyncratic respondents. The possibility of determining sample - independent item difficulties makes it possible to use the Rasch model for linking tests and tracking students’ progression. The use of Rasch model in PER is continuously increasing. We provide an overview and examples of its use and benefits, and also outline common mistakes/misconceptions made by researchers when considering the use of the Rasch model. We focus in particular on the question of how Rasch modeling can improve some common practices in PER, such as test construction, test evaluation, and calculation of student gain on PER diagnostic instruments

Control of T cell antigen reactivity via programmed TCR downregulation

The T cell antigen receptor (TCR) is unique in that its affinity for ligand is unknown before encounter and can vary by orders of magnitude. How the immune system regulates individual T cells that display very different reactivity to antigen remains unclear. Here we found that activated CD4(+) T cells, at the peak of clonal expansion, persistently downregulated their TCR expression in proportion to the strength of the initial antigen recognition. This programmed response increased the threshold for cytokine production and recall proliferation in a clone-specific manner and ultimately excluded clones with the highest antigen reactivity. Thus, programmed downregulation of TCR expression represents a negative feedback mechanism for constraining T cell effector function with a suitable time delay to thereby allow pathogen control while avoiding excess inflammatory damag

Measuring students’ conceptual understanding of wave optics: A Rasch modeling approach

Even graduate physics students have many misconceptions about basic wave optics phenomena. This suggests that there is much room for improvement of the traditional wave optics curriculum. An effective way for initiating a curriculum change is to reconsider and revise the expected learning outcomes and corresponding assessment instruments. By systematically enriching our wave optics instruction and assessment with conceptual tasks, we may increase the probability of students actively engaging in learning the conceptual aspects of wave optics. In this paper, we present the process of developing an item bank for measuring understanding of wave optics in typical introductory physics courses at universities. Thereby, the Rasch modeling approach has been used. The development of the item bank has been guided by results from multiple expert and student surveys, as well as from group interviews and think aloud interviews. Altogether 65 multiple-choice items with a single correct answer and three distractors have been prepared for field testing. Until now, 35 out of 65 items have been field tested by means of a paper and pencil survey which included 188 participants from five universities in Bosnia and Herzegovina, Croatia, and Slovenia. The field test showed that 32 out of 35 items have good psychometric characteristics and that they may be very useful for uncovering students’ misconceptions in wave optics

Distinct but Spatially Overlapping Intestinal Niches for Vancomycin-Resistant <i>Enterococcus faecium</i> and Carbapenem-Resistant <i>Klebsiella pneumoniae</i>

<div><p>Antibiotic resistance among enterococci and γ-proteobacteria is an increasing problem in healthcare settings. Dense colonization of the gut by antibiotic-resistant bacteria facilitates their spread between patients and also leads to bloodstream and other systemic infections. Antibiotic-mediated destruction of the intestinal microbiota and consequent loss of colonization resistance are critical factors leading to persistence and spread of antibiotic-resistant bacteria. The mechanisms underlying microbiota-mediated colonization resistance remain incompletely defined and are likely distinct for different antibiotic-resistant bacterial species. It is unclear whether enterococci or γ-proteobacteria, upon expanding to high density in the gut, confer colonization resistance against competing bacterial species. Herein, we demonstrate that dense intestinal colonization with vancomycin-resistant <i>Enterococcus faecium</i> (VRE) does not reduce <i>in vivo</i> growth of carbapenem-resistant <i>Klebsiella pneumoniae</i>. Reciprocally, <i>K</i>. <i>pneumoniae</i> does not impair intestinal colonization by VRE. In contrast, transplantation of a diverse fecal microbiota eliminates both VRE and <i>K</i>. <i>pneumoniae</i> from the gut. Fluorescence <i>in situ</i> hybridization demonstrates that VRE and <i>K</i>. <i>pneumoniae</i> localize to the same regions in the colon but differ with respect to stimulation and invasion of the colonic mucus layer. While VRE and <i>K</i>. <i>pneumoniae</i> occupy the same three-dimensional space within the gut lumen, their independent growth and persistence in the gut suggests that they reside in distinct niches that satisfy their specific <i>in vivo</i> metabolic needs.</p></div

<i>K</i>. <i>pneumoniae</i> and VRE achieve similar densities in the large intestine of co-colonized mice.

<p>Ampicillin-treated mice were inoculated with <i>K</i>. <i>pneumoniae</i> by oral gavage or left uninfected. Three days later, half of the <i>K</i>. <i>pneumoniae</i>-infected mice and the uninfected group were challenged with VRE. Microbiota composition of mice colonized with VRE alone (V), <i>K</i>. <i>pneumoniae</i> alone (K) or both (VK) was determined by sequencing of the V4-V5 region of the 16S rRNA genes. (A) Fecal microbiota composition at different time points post VRE challenge. (B) Ileal and cecal microbiota composition at day 21 of colonization. (A,B) Each stacked bar represents the average of five individually-housed mice per time point.</p

<i>K</i>. <i>pneumoniae</i> and VRE reside within the same intestinal regions but occupy distinct metabolic niches.

<p>(A-D) Spatial localization of <i>K</i>. <i>pneumoniae</i> and VRE in the colon. Colon sections from ampicillin-treated mice colonized for 21 days with <i>K</i>. <i>pneumoniae</i> alone (A), VRE alone (B) and <i>K</i>. <i>pneumoniae</i> together with VRE (C, D) were hybridized with probes specific for <i>K</i>. <i>pneumoniae</i> and Enterococcus. (D) VRE and <i>K</i>. <i>pneumoniae</i> islands (dashed circles and square) in the colonic lumen of co-colonized mice. (A-D) All sections were counterstained with Hoechst dye to visualize nuclei. Scale bars, 10 μm. Insets, 63X oil objective plus 4X digital zoom. Images are representative of at least 5 mice per group. (E) Minimum distance between neighboring bacteria determined by confocal microscopy. ns = non-significant; ****<i>P</i><0.0001, by the Mann-Whitney test.</p

Pre-colonization with VRE does not prevent colonization by <i>K</i>. <i>pneumoniae</i>.

<p>(A) Experimental design. Mice were treated with ampicillin for 29 days. On day 5 of ampicillin treatment, mice were inoculated with 5x10⁴ colony-forming units (CFU) of VRE by oral gavage or left uninfected. Three days later, half of the VRE-infected mice and the uninfected group were challenged with 5x10⁴ CFU of <i>K</i>. <i>pneumoniae</i> (Kpn). (B, C) CFU of <i>K</i>. <i>pneumoniae</i> (B) and VRE (C) were quantified in fecal pellets collected at different time points post <i>K</i>. <i>pneumoniae</i> inoculation. (D, E) Mice were sacrificed 21 days post <i>K</i>. <i>pneumoniae</i> challenge. <i>K</i>. <i>pneumoniae</i> (D) and VRE (E) burden was quantified in the luminal contents from the duodenum, ileum and cecum. L.O.D., limit of detection. Data were pooled from two independent experiments (n = 10 per group). (B-E) Data were analyzed by the Mann-Whitney test.</p