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George Bradford

Using Big Data to Predict Online Student Success | Inside Higher Ed - 0 views

www.insidehighered.com/...predict-online-student-success

higher-education analytics learning big-data predictive

shared by George Bradford on 01 Feb 12 - No Cached
  • Researchers have created a database that measures 33 variables for the online coursework of 640,000 students – a whopping 3 million course-level records.
  • Project Participants American Public University System Community College System of Colorado Rio Salado College University of Hawaii System University of Illinois-Springfield University of Phoenix
  • “What the data seem to suggest, however, is that for students who seem to have a high propensity of dropping out of an online course-based program, the fewer courses they take initially, the better-off they are.”
  • ...6 more annotations...
  • Phil Ice, vice president of research and development for the American Public University System and the project’s lead investigator.
  • Predictive Analytics Reporting Framework
  • Rio Salado, for example, has used the database to create a student performance tracking system.
  • The two-year college, which is based in Arizona, has a particularly strong online presence for a community college – 43,000 of its students are enrolled in online programs. The new tracking system allows instructors to see a red, yellow or green light for each student’s performance. And students can see their own tracking lights.
  • It measures student engagement through their Web interactions, how often they look at textbooks and whether they respond to feedback from instructors, all in addition to their performance on coursework.
  • The data set has the potential to give institutions sophisticated information about small subsets of students – such as which academic programs are best suited for a 25-year-old male Latino with strength in mathematics
  • George Bradford on 01 Feb 12
     
    New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time, according to findings from a research project that is challenging conventional wisdom about student success. But perhaps more important than that potentially game-changing nugget, researchers said, is how the project has chipped away at skepticism in higher education about the power of "big data." Researchers have created a database that measures 33 variables for the online coursework of 640,000 students - a whopping 3 million course-level records. While the work is far from complete, the variables help track student performance and retention across a broad range of demographic factors. The data can show what works at a specific type of institution, and what doesn't. That sort of predictive analytics has long been embraced by corporations, but not so much by the academy. The ongoing data-mining effort, which was kicked off last year with a $1 million grant from the Bill and Melinda Gates Foundation, is being led by WCET, the WICHE Cooperative for Educational Technologies.
George Bradford

College Degrees, Designed by the Numbers - Technology - The Chronicle of Higher Education - 0 views

chronicle.com/...132945

analytics learning_analytics data higher education chronicle college degrees designed numbers

shared by George Bradford on 19 Jul 12 - No Cached
  • Arizona State's retention rate rose to 84 percent from 77 percent in recent years, a change that the provost credits largely to eAdvisor.
  • Mr. Lange and his colleagues had found that by the eighth day of class, they could predict, with 70-percent accuracy, whether a student would score a C or better. Mr. Lange built a system, rolled out in 2009, that sent professors frequently updated alerts about how well each student was predicted to do, based on course performance and online behavior.
  • Rio Salado knows from its database that students who hand in late assignments and don't log in frequently often fail or withdraw from a course. So the software is more likely to throw up a red flag for current students with those characteristics.
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  • And in a cautionary tale about technical glitches, the college began sharing grade predictions with students last summer, hoping to encourage those lagging behind to step up, but had to shut the alerts down in the spring. Course revisions had skewed the calculations, and some predictions were found to be inaccurate. An internal analysis found no increase in the number of students dropping classes. An improved system is promised for the fall.
  • His software borrows a page from Netflix. It melds each student's transcript with thousands of past students' grades and standardized-test scores to make suggestions. When students log into the online portal, they see 10 "Course Suggestions for You," ranked on a five-star scale. For, say, a health-and-human-performance major, kinesiology might get five stars, as the next class needed for her major. Physics might also top the list, to satisfy a science requirement in the core curriculum.
  • Behind those recommendations is a complex algorithm, but the basics are simple enough. Degree requirements figure in the calculations. So do classes that can be used in many programs, like freshman writing. And the software bumps up courses for which a student might have a talent, by mining their records—grades, high-school grade-point average, ACT scores—and those of others who walked this path before.
  • The software sifts through a database of hundreds of thousands of grades other students have received. It analyzes the historical data to figure out how much weight to assign each piece of the health major's own academic record in forecasting how she will do in a particular course. Success in math is strongly predictive of success in physics, for example. So if her transcript and ACT score indicate a history of doing well in math, physics would probably be recommended over biology, though both satisfy the same core science requirement.
  • Every year, students in Tennessee lose their state scholarships because they fall a hair short of the GPA cutoff, Mr. Denley says, a financial swing that "massively changes their likelihood of graduating."
  • George Bradford on 19 Jul 12
     
    July 18, 2012 College Degrees, Designed by the Numbers By Marc Parry Illustration by Randy Lyhus for The Chronicle Campuses are places of intuition and serendipity: A professor senses confusion on a student's face and repeats his point; a student majors in psychology after a roommate takes a course; two freshmen meet on the quad and eventually become husband and wife. Now imagine hard data substituting for happenstance. As Katye Allisone, a freshman at Arizona State University, hunkers down in a computer lab for an 8:35 a.m. math class, the Web-based course watches her back. Answers, scores, pace, click paths-it hoovers up information, like Google. But rather than personalizing search results, data shape Ms. Allisone's class according to her understanding of the material.
George Bradford

Analytics in Higher Education: Establishing a Common Language | EDUCAUSE - 0 views

www.educause.edu/...245405

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shared by George Bradford on 17 May 12 - No Cached
  • George Bradford on 17 May 12
     
    Analytics in Higher Education: Establishing a Common Language Title: Analytics in Higher Education: Establishing a Common Language (ID: ELI3026) Author(s): Angela van Barneveld (Purdue University), Kimberly Arnold (Purdue University) and John P. Campbell (Purdue University) Topics: Academic Analytics, Action Analytics, Analytics, Business Analytics, Decision Support Systems, Learning Analytics, Predictive Analytics, Scholarship of Teaching and Learning Origin: ELI White Papers, EDUCAUSE Learning Initiative (ELI) (01/24/2012) Type: Articles, Briefs, Papers, and Reports
George Bradford

Learning process analytics - EduTech Wiki - 1 views

edutechwiki.unige.ch/...Learning_process_analytics

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shared by George Bradford on 03 Sep 12 - No Cached
  • George Bradford on 03 Sep 12
     
    "Introduction In this discussion paper, we define learning process analytics as a collection of methods that allow teachers and learners to understand what is going on in a' 'learning scenario, i.e. what participants work(ed) on, how they interact(ed), what they produced(ed), what tools they use(ed), in which physical and virtual location, etc. Learning analytics is most often aimed at generating predictive models of general student behavior. So-called academic analytics even aims to improve the system. We are trying to find a solution to a somewhat different problem. In this paper we will focus on improving project-oriented learner-centered designs, i.e. a family of educational designs that include any or some of knowledge-building, writing-to-learn, project-based learning, inquiry learning, problem-based learning and so forth. We will first provide a short literature review of learning process analytics and related frameworks that can help improve the quality of educational scenarios. We will then describe a few project-oriented educational scenarios that are implemented in various programs at the University of Geneva. These examples illustrate the kind of learning scenarios we have in mind and help define the different types of analytics both learners and teachers need. Finally, we present a provisional list of analytics desiderata divided into "wanted tomorrow" and "nice to have in the future"."
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