D. Vats and R. G. Baraniuk, "Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression," in Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
Abstract: In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), which transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-free sparse regression algorithm. More specifically, we prove that, as the problem size becomes large (in the number of variables and in the number of observations), PaTh performs accurate sparse regression, under appropriate conditions, without specifying a tuning parameter. In finite-dimensional settings, we demonstrate that PaTh can alleviate the computational burden of model selection algorithms by significantly reducing the search space of tuning parameters.
The above example illustrates the advantages of using PaTh on real data. The first figure applies the forward-backward (FoBa) sparse regression algorithm to the UCI crime data. The horizontal axis speciﬁes the sparsity level and the vertical axis speciﬁes the coeﬃcient values. The second ﬁgure applies PaTh to the solution path in the ﬁrst ﬁgure. PaTh reduces the total number of solutions from 50 (in the first figure) to 4 (in the second figure). We observe similar trends for the gene data (last two ﬁgures).
A. C. Butler, E. J. Marsh, J. P. Slavinsky, and R. G. Baraniuk, "Integrating Cognitive Science and Technology Improves Learning in a STEM Classroom," Educational Psychology Review, March 2014.
Preprint version of the paper
Abstract: The most effective educational interventions often face significant barriers to widespread implementation because they are highly specific, resource-intense, and/or require comprehensive reform. We argue for an alternative approach to improving education: leveraging technology and cognitive science to develop interventions that generalize, scale, and can be easily implemented within any curriculum. In a classroom experiment, we investigated whether three simple, but powerful principles from cognitive science could be combined to improve learning. Although implementing these principles only required a few small changes to standard practice in a college engineering course, it significantly increased student performance on exams. Our findings highlight the potential for developing inexpensive, yet effective educational interventions that can be implemented worldwide.
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) 256x256 image frame from a video acquired at full resolution. (b) 64x64 image frame directly reconstructed from STOne measurements at a rate 6.25% of the full-rate measurements. (c) 256x256 image frame recovered from STOne measurements at a rate 5% of the full-rate measurements. (d) 256x256 image frame recovered from STOne measurements at a rate 1% of the full-rate measurements.
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.
- 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
- 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.
- Richard Baraniuk, C. Sidney Burrus, Rice University
- Elizabeth Marsh, Andrew Butler, Duke University
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!
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.
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."
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!
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.
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!