Methods for Analyzing Pathways through a Physics Major

Physics Education Research frequently investigates what students studying physics do on small time scales (e.g. single courses, observations within single courses), or post-education time scales (e.g., what jobs do physics majors get?) but there is little research into how students get from the beginning to the end of a physics degree. Our work attempts to visualize students paths through the physics major, and quantitatively describe the students who take physics courses, receive physics degrees, and change degree paths into and out of the physics program at Michigan State University.Comment: submitted to Physics Education Research Conference Proceedings 201

Quasi-Periodic Oscillations and energy spectra from the two brightest Ultra-Luminous X-ray sources in M82

Ultra-Luminous X-ray sources are thought to be accreting black holes that might host Intermediate Mass Black Holes (IMBH), proposed to exist by theoretical studies, even though a firm detection (as a class) is still missing. The brightest ULX in M82 (M82 X-1) is probably one of the best candidates to host an IMBH. In this work we analyzed the data of the recent release of observations obtained from M82 X-1 taken by XMM-Newton. We performed a study of the timing and spectral properties of the source. We report on the detection of (46+-2) mHz Quasi-Periodic Oscillations (QPOs) in the power density spectra of two observations. A comparison of the frequency of these high-frequency QPOs with previous detections supports the 1:2:3 frequency distribution as suggested in other studies. We discuss the implications if the (46+-2) mHz QPO detected in M82 X-1 is the fundamental harmonic, in analogy with the High-Frequency QPOs observed in black hole binaries. For one of the observations we have detected for the first time a QPO at 8 mHz (albeit at a low significance), that coincides with a hardening of the spectrum. We suggest that the QPO is a milli-hertz QPO originating from the close-by transient ULX M82 X-2, with analogies to the Low-Frequency QPOs observed in black hole binaries.Comment: 9 pages (with 4 figures and 4 tables). Accepted for publication in MNRAS (26/09/13

Ultraluminous X-ray sources with flat-topped noise and QPO

We analyzed the X-ray power density spectra of five ultraluminous X-ray sources (ULXs) NGC5408 X-1, NGC6946 X-1, M82 X-1, NGC1313 X-1 and IC342 X-1 that are the only ULXs which display both flat-topped noise (FTN) and quasi-periodic oscillations (QPO). We studied the QPO frequencies, fractional root-mean-square (rms) variability, X-ray luminosity and spectral hardness. We found that the level of FTN is anti-correlated with the QPO frequency. As the frequency of the QPO and brightness of the sources increase, their fractional variability decreases. We propose a simple interpretation using the spherizarion radius, viscosity time and $\alpha$-parameter as basic properties of these systems. The main physical driver of the observed variability is the mass accretion rate which varies >3 between different observations of the same source. As the accretion rate decreases the spherization radius reduces and the FTN plus the QPO move toward higher frequencies resulting in a decrease of the fractional rms variability. We also propose that in all ULXs when the accretion rate is low enough (but still super-Eddington) the QPO and FTN disappear. Assuming that the maximum X-ray luminosity depends only on the black hole (BH) mass and not on the accretion rate (not considering the effects of either the inclination of the super-Eddington disc nor geometrical beaming of radiation) we estimate that all the ULXs have about similar BH masses, with the exception of M82 X-1, which might be 10 times more massive.Comment: 15 pages, 7 figures, accepted for publication in MNRA

Modeling student pathways in a physics bachelor's degree program

Physics education research has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University (MSU). Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students' declared gender and ethnicity play a much smaller role relative to the other features included the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.Comment: submitted to Physical Review Physics Education Researc

Developing the Next Generation of Physics Assessments

Science education at all levels is currently undergoing dramatic changes to its curricula and developing assessments for these new curricula is paramount. We have used the basis of many of these new changes (scientific practices, crosscutting concepts, and core ideas) to develop sets of criteria that can be used to guide assessment development for this new curriculum. We present a case study that uses the criteria we have developed to revise a traditional physics assessment item into an assessment item that is much more aligned with the goals of current transformation efforts. Assessment items developed using this criteria can be used to assess student learning of both the concepts and process of science.Comment: Revised version for PERC 2015 Conference Proceeding

Examining the relationship between student performance and video interactions

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.Comment: 4 pages, 1 figure, submitted to the PERC 2018 proceeding