No abstract

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in rewatching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

We introduce a systematic classification method for the analogs of phase transitions in finite systems. This completely general analysis, which is applicable to any physical system and extends towards the thermodynamic limit, is based on the microcanonical entropy and its energetic derivative, the inverse caloric temperature. Inflection points of this quantity signal cooperative activity and thus serve as distinct indicators of transitions. We demonstrate the power of this method through application to the long-standing problem of liquid-solid transitions in elastic, flexible homopolymers.PACS numbers: 05.20. Gg,36.40.Ei,82.60.Nh Structure formation processes are typically accompanied by nucleation transitions, where crystalline shapes form out of a liquid or vapor phase. Thus, nucleation is governed by finite-size and surface effects. For small physical systems, it is difficult to understand thermodynamic transitions of this type, as they strongly depend on system size.Cooperativity refers to collective changes in a statistically significant fraction of the degrees of freedom in a system, which transforms the system into a new macrostate. In the thermodynamic limit of an infinitely large system, the ensemble of macrostates sharing similar thermodynamic properties would be called a "phase" and the transformation a "phase transition". The description of such a transformation in a finite system is more subtle, as it cannot be described in the traditional Ehrenfest scheme of singularities in response quantities. However, statistical physics and thus thermodynamics are also valid for systems with no thermodynamic limit. Examples include the structure formation in small atomic clusters and all biomolecules. This is particularly striking for proteins, i.e., heterogeneous linear chains of amino acids. The fact that the individual biological function is connected with the geometrical shape of the molecule makes it necessary to discriminate unfolded (non-functional) and folded (functional) states. Although these systems are finite, they undergo a structural transition by passing a single (or more) free-energy barrier(s). Since these finite-system transitions exhibit strong similarities compared to phase transitions, we extend the terminology once defined in the thermodynamic limit to all systems exhibiting cooperative behavior.In this paper, we introduce a commonly applicable and simple method for the identification and classification of cooperative behavior in systems of arbitrary size by means of microcanonical thermodynamics [1]. It also includes the precise and straightforward analysis of the finite-size effects, which are important to a general understanding of the onset of phase transitions. This is in contrast to canonical approaches, where detailed information is lost by averaging out thermal fluctuations. Re-gaining information about finite-size effects in canonical schemes, e.g., by the investigation of the distribution of Lee-Yang zeros in the complex temperature plane [2] or by inverse Laplace transform [3...

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

Inspired by recent studies revealing unexpected pliability of semiflexible biomolecules like RNA and DNA, we systematically investigate the range of structural phases by means of a simple generic polymer model. Using a two-dimensional variant of Wang-Landau sampling to explore the conformational space in energy and stiffness within a single simulation, we identify the entire diversity of structures existing from the well-studied limit of flexible polymers to that of wormlike chains. We also discuss, in detail, the influence of finite-size effects in the formation of crystalline structures that are virtually inaccessible via conventional computational approaches.

The thermodynamic behavior of a continuous homopolymer is described using the Wang-Landau algorithm for chain lengths up to N=561. The coil-globule and liquid-solid transitions are analyzed in detail with traditional thermodynamic and structural quantities. The behavior of the coil-globule transition is well within theoretical and computational predictions for all chain lengths, while the behavior of the liquid-solid transition is much more susceptible to finite-size effects. Certain "magic number" lengths (N=13,55,147,309,561) , whose minimal energy states offer unique icosahedral geometries, are discussed along with chains residing between these special cases. The low temperature behavior near the liquid-solid transition is rich in structural transformations for certain chain lengths, showing many similarities to the behavior of classical clusters with similar interaction potentials. General comments are made on this size dependent behavior and how it affects transition behavior in this model.

Quality social science research and the privacy of human subjects require trust.

Because MOOCs offer complete logs of student activities for each student there is hope that it may be possible to find out which activities are the most useful for learning. We start this quest by examining correlations between time spent on specific course resources and various measures of student performance: score on assessments, skill as defined by Item Response Theory, improvement in skill over the period of the course, and conceptual improvement as measured by a prepost test. We study two MOOCs offered on edX.org by MIT faculty: Circuits and Electronics (6.002x) and Mechanics Review (8.MReV). Surprisingly, we find strong negative correlations in 6.002x between student skill and resource use; we attribute these findings to the fact that students with higher initial skills can do the exercises faster and with less time spent on instructional resources. We find weak or slightly negative correlations between relative improvement and resource use in 6.002x. The correlations with learning are stronger for conceptual knowledge in 8.MReV than with relative improvement, but similar for all course activities (except that eText checkpoint questions correlate more strongly with relative improvement). Clearly, the wide distribution of demographics and initial skill in MOOCs challenges us to isolate the habits of learning and resource use that correlate with learning for different students.