Everybody Must Get STOne

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STOne Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video,” 2013.

Abstract:  Compressive sensing enables the reconstruction of high-resolution signals from under-sampled data. While compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This article presents a new sensing framework that combines the advantages of both conventional and compressive sensing. Using the proposed STOne transform, measurements can be reconstructed instantly at Nyquist rates at any power-of-two resolution. The same data can then be “enhanced” to higher resolutions using compressive methods that leverage sparsity to “beat” the Nyquist limit. The availability of a fast direct reconstruction enables compressive measurements to be processed on small embedded devices. We demonstrate this by constructing a real-time compressive video camera.

(a)                                      (b)                                      (c)                                     (d)

The above example demonstrates reconstruction of high speed video from under-sampled measurements.  (a) 256×256 image frame from a video acquired at full resolution. (b) 64×64 image frame directly reconstructed from STOne measurements at a rate 6.25% of the full-rate measurements. (c) 256×256 image frame recovered from STOne measurements at a rate 5% of the full-rate measurements. (d) 256×256 image frame recovered from STOne measurements at a rate 1% of the full-rate measurements.

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Personalized Learning Workshop 2 April 2014

THE SCIENCE OF LEARNING:  Bridging the Laboratory-Classroom Divide

Revitalizing education at all levels and in all subject areas is a major priority in the United States.  To properly educate the leaders of tomorrow, we must move beyond the centuries-old, ingrained paradigm of education that views the process of learning as “one-way street” in which knowledge is transmitted from teacher to learner via paper textbooks and lectures.  Instead, we must provide learners with tools to effectively engage in self-regulated learning outside the classroom.

Despite the promise and some early successes in computer-based personalized learning, many important issues and challenges remain to be surmounted before personalized learning reaches the mainstream.  The goal of this annual workshop is to bring together the intellectual leaders of this new movement in order to exchange ideas, network, and plot a course to the future.

This year’s workshop will focus on how knowledge that has emerged from the science of learning can inform the development of personalized learning systems.  Machine learning algorithms and “big data” have the potential to revolutionize learning, but their application should be based on basic research findings from cognitive science, psychology, and education.  There is a pressing need to explore how research findings from the laboratory can be applied to facilitate learning in dynamic and complicated educational environments.  The workshop will feature leaders in the basic research on the science of learning who will discuss both their recent findings and the potential implications for personalized learning.

The scope of the workshop encompasses PK-12 through college and lifelong learning.  While primarily an in-person event, the lectures will also be webcast and archived for later viewing.

Confirmed Speakers

  • Michael Mozer, University of Colorado-Boulder
  • Kurt VanLehn, Arizona State University
  • Jeffrey Karpicke, Purdue University
  • Mark McDaniel, Washington University-St. Louis
  • Hal Pashler, University of California – San Diego

Workshop Sponsors

  • Rice University Office of the President,
  • Rice University Office of the Provost
  • George R. Brown School of Engineering
  • Ken Kennedy Institute
  • Rice Center for Digital Learning and Scholarship.

Workshop Organizers

  • Richard Baraniuk, C. Sidney Burrus, Rice University
  • Elizabeth Marsh, Andrew Butler, Duke University
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Connexions Conference 30 March-3 April 2014

You are invited to join us for CNX 2014, at Rice University in Houston, Texas. Every year, this conference brings together leading policy, academic and technology experts to discuss the future of open education resources (OER) as well as the technologies that are making this future a possibility.

The conference theme, “Making OER Work,” is an opportunity for faculty, instructional designers, librarians, administrators, students and industry leaders to focus on pragmatic solutions in open education. The conference also marks a new chapter in Connexions, where we plan to unveil the upcoming Connexions authoring and sharing platform, created in partnership with Google, that will make frictionless remix of educational materials a reality.

At this year’s conference, March 31 through April 3, 2014, we invite you to join us as we further accelerate the progress of the OER community and unveil exciting new digital publishing technologies.  More information is available here. See you there!

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The Future of Online Education

An interview in a series produced by the Skoll World Forum with the participation of today’s leading thinkers and innovators in education.  All of the contributors represent projects that have won WISE Awards, which recognize innovative solutions in overcoming barriers to education. This series aims to shed light on those projects that have helped provide access to quality education around the world.

Read the full interview here.

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OpenStax College @ Consumer Electronics Show

OpenStax College was featured in a panel discussion on “The Next Edition of Digital Textbooks and Courseware” at the 2014 TransformingEDU Summit of the Consumer Electronics Show in Las Vegas.  Watch it here.

“Entire states have adopted digital texts. University libraries are becoming repositories of digital content.  The question is no longer whether texts will go digital—they are.  Now we can ask what is working, what isn’t and what the next generation of digital texts will offer.”

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ELEC301x – Discrete Time Signals and Systems

ELEC301x – Discrete Time Signals and Systems

Enter the world of signal processing: analyze and extract meaning from the signals around us!

About the Course:  Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data? Students will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. Students will apply these concepts to build a digital audio synthesizer in MATLAB. Prerequisites include strong problem solving skills, the ability to understand mathematical representations of physical systems, and advanced mathematical background (one-dimensional integration, matrices, vectors, basic linear algebra, imaginary numbers, and sum and series notation). This course is an excerpt from an advanced undergraduate class at Rice University taught to all electrical and computer engineering majors.

Sign up now and join in the fun!

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"Opening Education" in NAE’s The Bridge

R. G. Baraniuk, “Opening Education,” to appear in The Bridge, National Academy of Engineering, 2013.

Abstract:  The world is increasingly connected, yet educational systems cling to the disconnected past. The open education movement provides new mechanisms to democratize education by interconnecting ideas, learners, and instructors in new kinds of constructs that replace traditional textbooks, courses, and certifications. Open education has the potential to realize the dream of providing not only universal access to all the world’s knowledge but also the tools required to acquire it. The result will be a revolutionary advance in the world’s standard of education at all levels.

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SPARFA – Sparse Factor Analysis for Learning and Content Analytics

A. S. Lan, A. E. Waters, C. Studer, R. G. Baraniuk, “Sparse Factor Analysis for Learning and Content Analytics,” to appear in Journal of Machine Learning Research, 2014

Abstract:  We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner’s knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question’s intrinsic difficulty. We estimate these factors given the graded responses to a collection of questions. The underlying estimation problem is ill-posed in general, especially when the only a subset of the questions are answered. The key observation that enables a well-posed solution is the fact that typical educational domains of interest involve only a small number of key concepts. Leveraging this observation, we develop both a bi-convex maximum-likelihood and a Bayesian solution to the resulting SPARse Factor Analysis (SPARFA) problem. We also incorporate user-defined tags on questions to facilitate the interpretability of the estimated factors. Experiments with synthetic and real-world data demonstrate the efficacy of our approach. Finally, we make a connection between SPARFA and noisy, binary-valued (1-bit) dictionary learning that is of independent interest.

The above example illustrates the result of applying SPARFA to data from a grade 8 science course in STEMscopes, an online science curriculum program. The data input to SPARFA consisted solely of whether a student answered a given potential homework or exam question correctly or incorrectly. From these limited and quantized data, SPARFA automatically estimates (a) a collection (in this case five) of abstract “concepts” that underlie the course (“Concept 3” is illustrated here); (b) a graph that links each question (rectangular box) to one or more of the concepts (circles), with thicker links indicating a stronger association with the concept; (c) the intrinsic difficulty of each question,  indicated by the number in each box; (d) descriptive word tags drawn from the text of the questions, their solutions, and instructor-provided metadata that make each concept interpretable (as shown for Concept 3); and (e) each student’s knowledge profile, which indicates both estimated knowledge of each concept and concepts ripe for remediation or enrichment.

Some follow-on papers that extend the SPARFA framework.
Get your SPARFA merchandise while it’s hot!

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Personalized Learning Workshop @ Rice 2013

Revitalizing education at all levels and in all subject areas is a major global priority. In order to properly educate the leaders of tomorrow, we must move beyond the centuries-old, ingrained paradigm of education that views the process of learning as “one-way street” in which knowledge is transmitted from teacher to learner via paper textbooks and lectures. Instead, we must provide learners with tools to effectively engage in self-regulated learning outside the classroom.

Over the past decades, significant progress has been made on computer-based personalized learning that is responsive to the needs, skills, and characteristics of individual students. A personalized learning system closes the learning feedback loop by continuously monitoring and analyzing learner interactions with learning resources in order to assess progress, and providing timely remediation, enrichment, or practice based on that analysis. Recently, learning analytics and personalized learning systems have leapt from the research lab to the marketplace. Indeed, much of the ed-tech startup activity and investment has been in this space.

Despite this early success, many important issues and challenges remain to be surmounted before personalized learning reaches the mainstream. The goal of this (annual) workshop was to bring together the intellectual leaders of this new movement in order to exchange ideas, network, and plot a course to the future. A particular focus was on how machine learning and “big data” have the potential to create new efficiencies in time and cost and significantly improve learning outcomes.

The workshop took place on 22 April 2013 on the Rice University campus.

Presenters
David Kuntz, VP-Research for Knewton
Steven Ritter, VP-Research for Carnegie Learning
Jascha Sohl-Dickstein, Khan Academy
David Pritchard, MIT
Neil Heffernan, WPI
Winslow Burleson, ASU
David Eagleman, BCM
Dan Wallach, Rice
Anna Rafferty, UC-Berkeley
Zach Pardos, MIT
Andrew Butler, Duke
Richard Baraniuk, Rice

Archived webcast is available here

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Leiden Ranks Rice No. 1 for Engineering

Rice University is ranked No. 1 among the world’s top universities in the field of natural sciences and engineering for the quality and impact of its scientific publications, according to the Leiden rankings for 2013.

The Leiden rankings measure the scientific performance of 500 major universities around the world. The rankings are calculated by the Centre for Science and Technology Studies at Leiden University in Netherlands. The 2013 rankings are based on indexed publications from 2008 to 2011 from the Web of Science bibliographic database produced by Thomson Reuters. Web of Science is a reference tool for retrieving accurate citation counts.

Read more

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