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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.”
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  • 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

[!!!] Penetrating the Fog: Analytics in Learning and Education (EDUCAUSE Review) | EDUC... - 0 views

www.educause.edu/...235017

higher-education analytics learning educause trends

shared by George Bradford on 01 Feb 12 - No Cached
  • Continued growth in the amount of data creates an environment in which new or novel approaches are required to understand the patterns of value that exist within the data.
  • learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
  • Academic analytics, in contrast, is the application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels.
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  • Course-level:
  • Educational data-mining
  • Intelligent curriculum
  • Adaptive content
  • the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.
  • Mobile devices
  • social media monitoring tools (e.g., Radian6)
  • Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.
    • George Bradford
      George Bradford on 01 Feb 12
       
      See Bradford - Brief vision of the semantic web as being used to support future learning: http://heybradfords.com/moonlight/research-resources/SemWeb_EducatorsVision 
    • George Bradford
      George Bradford on 01 Feb 12
       
      See Peter Goodyear's work on the Ecology of Sustainable e-Learning in Education.
  • How “real time” should analytics be in classroom settings?
  • Adaptive learning
  • EDUCAUSE Review, vol. 46, no. 5 (September/October 2011)
  • Penetrating the Fog: Analytics in Learning and Education
  • George Bradford on 01 Feb 12
     
    Attempts to imagine the future of education often emphasize new technologies-ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that we can't actually touch or see: big data and analytics. Basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision-making improves organizational output and productivity.1 For many leaders in higher education, however, experience and "gut instinct" have a stronger pull.
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

SpringerLink - Abstract - Dr. Fox Rocks: Using Data-mining Techniques to Examine Studen... - 0 views

www.springerlink.com/...r825505806261k73

student_satisfaction research data-mining SEI evaluate instruction analytics

shared by George Bradford on 13 Feb 12 - No Cached
  • George Bradford on 13 Feb 12
     
    Abstract Few traditions in higher education evoke more controversy, ambivalence, criticism, and, at the same time, support than student evaluation of instruction (SEI). Ostensibly, results from these end-of-course survey instruments serve two main functions: they provide instructors with formative input for improving their teaching, and they serve as the basis for summative profiles of professors' effectiveness through the eyes of their students. In the academy, instructor evaluations also can play out in the high-stakes environments of tenure, promotion, and merit salary increases, making this information particularly important to the professional lives of faculty members. At the research level, the volume of the literature for student ratings impresses even the most casual observer with well over 2,000 studies referenced in the Education Resources Information Center (ERIC) alone (Centra, 2003) and an untold number of additional studies published in educational, psychological, psychometric, and discipline-related journals. There have been numerous attempts at summarizing this work (Algozzine et al., 2004; Gump, 2007; Marsh & Roche, 1997; Pounder, 2007; Wachtel, 1998). Student ratings gained such notoriety that in November 1997 the American Psychologist devoted an entire issue to the topic (Greenwald, 1997). The issue included student ratings articles focusing on stability and reliability, validity, dimensionality, usefulness for improving teaching and learning, and sensitivity to biasing factors, such as the Dr. Fox phenomenon that describes eliciting high student ratings with strategies that reflect little or no relationship to effective teaching practice (Ware & Williams, 1975; Williams & Ware, 1976, 1977).
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