983 research outputs found
Learning analytics as a "middle space"
No full textLearning Analytics, an emerging field concerned with an- alyzing the vast data “given off” by learners in technology supported settings to inform educational theory and prac- tice, has from its inception taken a multidisciplinary ap- proach that integrates studies of learning with technological capabilities. In this introduction to the Proceedings of the Third International Learning Analytics & Knowledge Con- ference, we discuss how Learning Analytics must function in the “middle space” where learning and analytic concerns meet. Dialogue in this middle space involves diverse stake- holders from multiple disciplines with various conceptions of the agency and nature of learning. We hold that a sin- gularly unified field is not possible nor even desirable if we are to leverage the potential of this diversity, but progress is possible if we support “productive multivocality” between the diverse voices involved, facilitated by appropriate use of boundary objects. We summarize the submitted papers and contents of these Proceedings to characterize the voices and topics involved in the multivocal discourse of Learning Analytics.sponsorship: Research Foundation Flanders (FWO)status: Publishe
Collaborative representations: supporting face to face and online knowledge-building discourse
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The turbulent dissipation rate from PIV measurements
No full textThe result of a particle-image velocimetry (PIV) measurement is a velocity field averaged over interrogation windows. This severely affects the measurement of small-scale turbulence quantities when the interrogation window size is much larger than the smallest length scale in turbulence. A direct measurement of the dissipation rate demands the measurement of gradients of the velocity field, which are now underestimated because the small-scale motion is not resolved. A popular procedure is to relate the statistical properties of the measured, but underresolved gradients to those of the true ones, invoking a large-eddy argument [3]. We show that the used proportionality constant, the Smagorinsky constant, should depend on the window overlap, on the used elements of the strain tensor, and on the way in which derivatives are approximate
LAK 2013 : third international conference on learning analytics and knowledge : Leuven, Belgium, April 08-12, 2013
No full textWelcome to the third edition of the Learning Analytics and Knowledge conference. This year, the medieval and, at the same time, modern city of Leuven, Belgium is the venue where researchers and practitioners of this exciting field come together to discuss current status and future trends. Similar to Leuven, Learning Analytics is an old and new field at the same time. Old, because it deals with a problem that exists since Plato's times: how to improve the way students learn. New, because the tools used to achieve this goal, like Big Data and natural language processing, were not feasible merely 10 years ago. Leuven is also the home of beautiful centuries old buildings filled with young, smart and active students. In Learning Analytics, we can also find established researchers in the fields of Educational Research and Technology-Enhanced Learning, collaborating with a large contingent of new and promising researchers that could be called Learning Data Scientists
How the dispersion of a droplet cloud depends on its initial size
No full textA cloud of droplets evolves under the influence of strong turbulence. The droplets are made from a phosphorescent fluid. From this cloud we select at t = 0 a narrow line by exciting the droplets with a UV laser, which causes them to glow for a few milliseconds. The dispersion of this line is followed in time using a fast intensified camera. A large range of droplet sizes (Stokes number St) was measured. It appears that lines with St \approx 1 disperse faster than a line of fluid tracers. Lines of droplets which are narrowest initially, spread fastest
Reverend William M. Paden, D.D.
No full textThis photograph is a portrait, featured in a publication, of Reverend William M. Paden, D.D. He is wearing a dark suit, light shirt, and a dark tie with polka dots. His hair is parted in the middle and he wears a mustache. He is also wearing wire-rimmed glasses. The background is a medium gray shade.The photograph is in good condition, except for a slight wrinkle in the bottom right corner. On the back the paper appears torn because of glue and there is a small pink spot in the bottom half of the photograph. The following text is printed at the bottom of the photograph: "Reverend William M. Paden D.D. Beloved, purposeful, Missioner of Good Cheer. Leader of Presbyterianism in Utah for thirty-four years, as Pastor, Executive, Author and Counselor. Together by the grace of God, we enter the forward-looking years, growing ever richer in Christian faith and service." The Archives, Giovale Library, Westminster College, has a collection of materials authored by William Paden and also materials about him. Additional biographic information about Dr Paden is available in: Brackenridge, R. Douglas. Westminster College of Salt Lake City. Logan, Utah: Utah State University Press, 1998, pp. 118, 121, 124, 140
LAK 2013 : third international conference on learning analytics and knowledge : Leuven, Belgium, April 08-12, 2013
No full textWelcome to the third edition of the Learning Analytics and Knowledge conference. This year, the medieval and, at the same time, modern city of Leuven, Belgium is the venue where researchers and practitioners of this exciting field come together to discuss current status and future trends. Similar to Leuven, Learning Analytics is an old and new field at the same time. Old, because it deals with a problem that exists since Plato's times: how to improve the way students learn. New, because the tools used to achieve this goal, like Big Data and natural language processing, were not feasible merely 10 years ago. Leuven is also the home of beautiful centuries old buildings filled with young, smart and active students. In Learning Analytics, we can also find established researchers in the fields of Educational Research and Technology-Enhanced Learning, collaborating with a large contingent of new and promising researchers that could be called Learning Data Scientists
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