Workshop on methodology in learning analytics (MLA)

Learning analytics is an interdisciplinary and inclusive field, a fact which makes the establishment of methodological norms both challenging and important. This community-building workshop intends to convene methodology-focused researchers to discuss new and established approaches, comment on the state of current practice, author pedagogical manuscripts, and co-develop guidelines to help move the field forward with quality and rigor

Dressing Up the Kink

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.Comment: 13 pages, latex, 3 eps figures; revised with yet additional references, minor rewordin

A LAK of Direction Misalignment Between the Goals of Learning Analytics and its Research Scholarship

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments

The self-consistent bounce: an improved nucleation rate

We generalize the standard computation of homogeneous nucleation theory at zero temperature to a scenario in which the bubble shape is determined self-consistently with its quantum fluctuations. Studying two scalar models in 1+1 dimensions, we find the self-consistent bounce by employing a two-particle irreducible (2PI) effective action in imaginary time at the level of the Hartree approximation. We thus obtain an effective single bounce action which determines the rate exponent. We use collective coordinates to account for the translational invariance and the growth instability of the bubble and finally present a new nucleation rate prefactor. We compare the results with those obtained using the standard 1-loop approximation and show that the self-consistent rate can differ by several orders of magnitude.Comment: 28 pages, revtex, 7 eps figure

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

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, most notably by more frequent exams, show consistently high usage of the materials with far less “cramming” before exams.National Science Foundation (U.S.) (Grant DUE-1044294)Google (Firm

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

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, most notably by more frequent exams, show consistently high usage of the materials with far less “cramming” before exams.National Science Foundation (U.S.) (Grant DUE-1044294)Google (Firm

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 \lambda \phi^4 + g \phi^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.Comment: 12 pages, multiple figure

For whom is data literacy empowering? An awareness-action typology

Building on recent empowerment perspectives on data literacy, we examine how students and working adults talk about their understanding of data and report on their own personal-data-related practices. Through a deductive and inductive analysis of interviews with 19 subjects ranging from middle school to middle age, we find that awareness and action with respect to data consumption and production do not necessarily increase in tandem. For example, being more aware of the data that can be used to track them does not make individuals more likely to take action to manage their personal data. While some feel anxiety about the gap between knowledge and action, others resolve the tension by choosing not to care. These findings are synthesized in a typology of personas in the space of data awareness and action. We investigate the relationship between age and educational attainment with location in this awareness-action space and discuss implications for data literacy education

Théorie de la réponse à l'item multidimensionnelle façon filtrage collaboratif

International audienceThis paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course

Who does what in a massive open online course?

Massive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants behaved in 6.002x - Circuits and Electronics, the first course in MITx (now edX). We divide participants into tranches based on the extent of their assessment activities, ranging from browsers (who constituted ~ 76% of the participants but accounted for only 8% of the total time spent in the course) to certificate-earners (7% of theparticipants who accounted for 60% of the total time). We examine how the certificate earners allocated their time amongst the various course components and study 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 use of various course components, and transitions among them, influence learning in MOOCs.National Science Foundation (U.S.) (DUE-1044294