Yoav Bergner

Who does what in a massive open online course

Student-participation data from the inaugural MITx (now edX) course---6.002x: Circuits and Electronics---unpacks MOOC student behavior.

Integrable supersymmetric fluid mechanics from superstrings

Abstract 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.

A step beyond the bounce: Bubble dynamics in quantum phase transitions

We study the dynamical evolution of a phase interface or bubble in the context of a

Longitudinal engagement, performance, and social connectivity: a MOOC case study using exponential random graph models

\ensuremath{\lambda}{\ensuremath{\varphi}}^{4}+g{\ensuremath{\varphi}}^{6}

Estimation of ability from homework items when there are missing and/or multiple attempts

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

Analyzing the impact of course structure on electronic textbook use in blended introductory physics courses

1+1

Dressing up the kink

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.

Dynamic Bayesian Network Models for Peer Tutoring Interactions

This paper explores a longitudinal approach to combining engagement, performance and social connectivity data from a MOOC using the framework of exponential random graph models (ERGMs). The idea is to model the social network in the discussion forum in a given week not only using performance (assignment scores) and overall engagement (lecture and discussion views) covariates within that week, but also on the same person-level covariates from adjacent previous and subsequent weeks. We find that over all eight weekly sessions, the social networks constructed from the forum interactions are relatively sparse and lack the tendency for preferential attachment. By analyzing data from the second week, we also find that individuals with higher performance scores from current, previous, and future weeks tend to be more connected in the social network. Engagement with lectures had significant but sometimes puzzling effects on social connectivity. However, the relationships between social connectivity, performance, and engagement weakened over time, and results were not stable across weeks.

Self-consistent bounce: An improved nucleation rate

Scoring of student item response data from online courses and especially massively open online courses (MOOCs) is complicated by two challenges, potentially large amounts of missing data and allowances for multiple attempts to answer. Approaches to ability estimation with respect to both of these issues are considered using data from a large-enrollment electrical engineering MOOC. The allowance of unlimited multiple attempts sets up a range of observed score and latent-variable approaches to scoring the constructed response homework. With respect to missing data, two classical approaches are discussed, treating omitted items as incorrect or missing at random (MAR). These treatments turn out to have slightly different interpretations depending on the scoring model. In all, twelve different homework scores are proposed based on combinations of scoring model and missing data handling. The scores are computed and correlations between each score and the final exam score are compared, with attention to different populations of course participants.

Deep making: curricular modules for transferable content-knowledge and scientific literacy in makerspaces and FabLabs

We investigate how elements of course structure (i.e., the frequency of assessments as well as the sequencing and weight of course resources) influence the usage patterns of electronic textbooks (e-texts) in introductory physics courses. Specifically, we analyze the access logs of courses at Michigan State University and the Massachusetts Institute of Technology, each of which deploy e-texts as primary or secondary texts in combination with different formative assessments (e.g., embedded reading questions) and different summative assessment (exam) schedules. As such studies are frequently marred by arguments over what constitutes a “meaningful” interaction with a particular page (usually judged by how long the page remains on the screen), we consider a set of different definitions of “meaningful” interactions. We find that course structure has a strong influence on how much of the e-texts students actually read, and when they do so. In particular, courses that deviate strongly from traditional structures,...