massive open online courses (MOOCs) collect valuable data on student learning behavior; essentially complete records of all student interactions in a selfcontained learning environment, with the benefit of large sample sizes. Here, we offer an overview of how the 108,000 participants behaved in 6.002x -Circuits and Electronics, the first course in MITx (now edX) in the Spring 2012 semester. We divided participants into tranches based on the extent of their assessment activities, ranging from browsers (constituting ~76% of the participants but only 8% of the total time spent in the course) to certificate earners (7% of participants who accounted for 60% of total time). We examined how the certificate earners allocated their time among the various course components and what fraction of each they accessed. We analyze transitions between course components, showing how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how various course components, and transitions among them, influence learning in MOOCs.Though free online courses are not new, 8 they have reached an unprecedented scale since late 2011. Three organizations-Coursera, edX, and Udacity-have released MOOCs 13 drawing more than 100,000 registrants per course. Numbers from these three initiatives have since grown to more than 100 courses and three million total registrants, resulting in 2012 being dubbed "The Year of the MOOC" by the New York Times. 16 Though there has been much speculation regarding how these initiatives may reshape higher education, 6,12,20 little analysis has been published to date describing student behavior or learning in them.Our main objective here is to show how the huge amount of data available in MOOCs offers a unique research opportunity, a means to study detailed student behavior in a self-contained learning environment throughout an Who Does What in a massive open online course?Data collected in moocs provides insight into student behavior, from weekly e-textbook reading habits to contextdependent use of learning resources when solving problems. in 6.002x, 76% of participants were browsers who collectively accounted for only 8% of time spent in the course, whereas, the 7% of certificate-earning participants averaged 100 hours each and collectively accounted for 60% of total time.Students spent the most time per week interacting with lecture videos and homework, followed by discussion forums and online laboratories; however, interactions with the videos and lecture questions were distinctly bimodal, with half the certificate earners accessing less than half of these resources.illuStration by anthony freda

Following the construction of a model for the planar supersymmetric Chaplygin gas, supersymmetric fluid mechanics in (1+1)-dimensions is obtained from the light-cone parametrized Nambu-Goto superstring in (2+1)-dimensions. The lineal model is completely integrable and can be formulated neatly using Riemann coordinates. Infinite towers of conserved charges and supercharges are exhibited. They form irreducible representations of a dynamical (hidden) SO(2, 1) symmetry group.

Many quantum field theoretical models possess non-trivial solutions which are stable for topological reasons. We construct a self-consistent example for a self-interacting scalar field-the quantum (or dressed) kink-using a two particle irreducible effective action in the Hartree approximation.This new solution includes quantum fluctuations determined self-consistently and nonperturbatively at the 1-loop resummed level and allowed to backreact on the classical mean-field profile.This dressed kink is static under the familiar Hartree equations for the time evolution of quantum fields. Because the quantum fluctuation spectrum is lower lying in the presence of the defect, the quantum kink has a lower rest energy than its classical counterpart. However its energy is higher than well-known strict 1-loop results, where backreaction and fluctuation self-interactions are omitted. We also show that the quantum kink exists at finite temperature and that its profile broadens as temperature is increased until it eventually disappears.

We study the dynamical evolution of a phase interface or bubble in the context of a λφ 4 + gφ 6 scalar quantum field theory. We use a self-consistent mean-field approximation derived from a 2PI effective action to construct an initial value problem for the expectation value of the quantum field and two-point function. We solve the equations of motion numerically in (1+1)-dimensions and compare the results to the purely classical evolution. We find that the quantum fluctuations dress the classical profile, affecting both the early time expansion of the bubble and the behavior upon collision with a neighboring interface.

This article reviews how National Assessment of Educational Progress (NAEP) has come to collect and analyze data about cognitive and behavioral processes (process data) in the transition to digital assessment technologies over the past two decades. An ordered five-level structure is proposed for describing the uses of process data. The levels in this hierarchy range from ignoring the processes (i.e., counting only the outcomes), to incorporating process data as auxiliary or essential in addition to the outcome, to modeling the process as the outcome itself, either holistically in a rubric score or in a measurement model that accounts for sequential dependencies. Historical examples of these different uses are described as well as recent results using nontraditional analytical approaches. In the final section, speculative future directions incorporating state-of-the-art technologies and analysis methods are described with an eye toward hard-to-measure constructs such as higher order problem-solving and collaboration.

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Collaborative problem solving (CPS) is a critical competency in a variety of contexts, including the workplace, school, and home. However, only recently have assessment and curriculum reformers begun to focus to a greater extent on the acquisition and development of CPS skill. One of the major challenges in psychometric modeling of CPS is collecting large‐scale data on teams and processes. In this study, we explore the use of agent‐based modeling (ABM) to model the CPS process, test the sensitivity of outcomes to different population characteristics, and generate simulated data that can provide a novel means by which to refine and develop psychometric models. Methods of adapting trait‐based stochastic processes to a specific task are described, and preliminary results are presented.

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