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George Bradford

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

  • 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
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    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

  • 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."
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    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

http://www.sinclair.edu/support/success/ea/ - 0 views

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    Early Alert Program The Early Alert classroom assistance program at Sinclair Community College is an intervention program teaming faculty, counselors, and advisors together in order to promote the success of students facing challenges.   An Overview:    Early Alert is an intervention program that allows for faculty to notify advisors/counselors of issues that may affect the success of a student.  It is a simple way of assisting students in difficulty find the help they need while taking very little time. Web-based Early Alert notifications are easy ways to promote the retention efforts of the College and the success of students. Utilized currently in all DEV courses, English 111, select Math courses, and SCC 101 courses.
George Bradford

Assessment and Analytics in Institutional Transformation (EDUCAUSE Review) | EDUCAUSE - 0 views

  • At the University of Maryland, Baltimore County (UMBC), we believe that process is an important factor in creating cultural change. We thus approach transformational initiatives by using the same scholarly rigor that we expect of any researcher. This involves (1) reviewing the literature and prior work in the area, (2) identifying critical factors and variables, (3) collecting data associated with these critical factors, (4) using rigorous statistical analysis and modeling of the question and factors, (5) developing hypotheses to influence the critical factors, and (6) collecting data based on the changes and assessing the results.
  • among predominantly white higher education institutions in the United States, UMBC has become the leading producer of African-American bachelor’s degree recipients who go on to earn Ph.D.’s in STEM fields. The program has been recognized by the National Science Foundation and the National Academies as a national model.
  • UMBC has recently begun a major effort focused on the success of transfer students in STEM majors. This effort, with pilot funding from the Bill and Melinda Gates Foundation, will look at how universities can partner with community colleges to prepare their graduates to successfully complete a bachelor’s degree in a STEM field.
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  • Too often, IT organizations try to help by providing an analytics “dashboard” designed by a vendor that doesn’t know the institution. As a result, the dashboard indicators don’t focus on those key factors most needed at the institution and quickly become window-dressing.
  • IT organizations can support assessment by showing how data in separate systems can become very useful when captured and correlated. For example, UMBC has spent considerable effort to develop a reporting system based on our learning management system (LMS) data. This effort, led from within the IT organization, has helped the institution find new insights into the way faculty and students are using the LMS and has helped us improve the services we offer. We are now working to integrate this data into our institutional data warehouse and are leveraging access to important demographic data to better assess student risk factors and develop interventions.
  • the purpose of learning analytics is “to observe and understand learning behaviors in order to enable appropriate interventions.
  • the 1st International Conference on Learning Analytics and Knowledge (LAK) was held in Banff, Alberta, Canada, in early 2011 (https://tekri.athabascau.ca/analytics/)
  • At UMBC, we are using analytics and assessment to shine a light on students’ performance and behavior and to support teaching effectiveness. What has made the use of analytics and assessment particularly effective on our campus has been the insistence that all groups—faculty, staff, and students—take ownership of the challenge involving student performance and persistence.
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    Assessment and analytics, supported by information technology, can change institutional culture and drive the transformation in student retention, graduation, and success. U.S. higher education has an extraordinary record of accomplishment in preparing students for leadership, in serving as a wellspring of research and creative endeavor, and in providing public service. Despite this success, colleges and universities are facing an unprecedented set of challenges. To maintain the country's global preeminence, those of us in higher education are being called on to expand the number of students we educate, increase the proportion of students in science, technology, engineering, and mathematics (STEM), and address the pervasive and long-standing underrepresentation of minorities who earn college degrees-all at a time when budgets are being reduced and questions about institutional efficiency and effectiveness are being raised.
George Bradford

alpha lab research network - Persistence - 0 views

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    "PRODUCTIVE PERSISTENCE: A "PRACTICAL" THEORY OF COMMUNITY COLLEGE STUDENTS SUCCESS The Carnegie Foundation's Productive Persistence initiative is a practical theory of the causes of successfully completing coursework at a community COLLEGE-or, in our terms, the "drivers" of successful course completion.  The term "Productive Persistence" refers to both the tenacity to persist, and also the ability to use good strategiesto productively engage with the course materials. "
George Bradford

NSSE Home - 0 views

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    National Survey of Student Engagement What is student engagement? Student engagement represents two critical features of collegiate quality. The first is the amount of time and effort students put into their studies and other educationally purposeful activities. The second is how the institution deploys its resources and organizes the curriculum and other learning opportunities to get students to participate in activities that decades of research studies show are linked to student learning. What does NSSE do? Through its student survey, The College Student Report, NSSE annually collects information at hundreds of four-year Colleges and universities about student participation in programs and activities that institutions provide for their learning and personal development. The results provide an estimate of how undergraduates spend their time and what they gain from attending College. NSSE provides participating institutions a variety of reports that compare their students' responses with those of students at self-selected groups of comparison institutions. Comparisons are available for individual survey questions and the five NSSE Benchmarks of Effective Educational Practice. Each November, NSSE also publishes its Annual Results, which reports topical research and trends in student engagement results. NSSE researchers also present and publish research findings throughout the year.
George Bradford

Analytics in Higher Education - Benefits, Barriers, Progress, and Recommendations (EDUCAUSE) 2012 - 0 views

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    Jacqueline Bichsel - 2012 EDUCAUSE Many colleges and universities have demonstrated that analytics can help significantly advance an institution in such strategic areas as resource allocation, student success, and finance. Higher education leaders hear about these transformations occurring at other institutions and wonder how their institutions can initiate or build upon their own analytics programs. Some question whether they have the resources, infrastructure, processes, or data for analytics. Some wonder whether their institutions are on par with other in their analytics endeavors. It is within that context that this study set out to assess the current state of analytics in higher education, outline the challenges and barriers to analytics, and provide a basis for benchmarking progress in analytics.
George Bradford

Seeking Evidence of Impact: Opportunities and Needs (EDUCAUSE Review) | EDUCAUSE - 0 views

  • Conversations with CIOs and other senior IT administrators reveal a keen interest in the results of evaluation in teaching and learning to guide fiscal, policy, and strategic decision-making. Yet those same conversations reveal that this need is not being met.
  • gain a wider and shared understanding of “evidence” and “impact” in teaching and learning
  • establish a community of practice
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  • provide professional-development opportunities
  • explore successful institutional and political contexts
  • establish evidence-based practice
  • The most important reason is that in the absence of data, anecdote can become the primary basis for decision-making. Rarely does that work out very well.
  • autocatalytic evaluation process—one that builds its own synergy.
  • We live by three principles: uncollected data cannot be analyzed; the numbers are helped by a brief and coherent summary; and good graphs beat tables every time.
  • Reports and testimonies from faculty and students (57%) Measures of student and faculty satisfaction (50%) Measures of student mastery (learning outcomes) (41%) Changes in faculty teaching practice (35%) Measures of student and faculty engagement (32%)
  • The survey results also indicate a need for support in undertaking impact-evaluation projects.
  • Knowing where to begin to measure the impact of technology-based innovations in teaching and learning Knowing which measurement and evaluation techniques are most appropriate Knowing the most effective way to analyze evidence 
  • The challenge of persuasion is what ELI has been calling the last mile problem. There are two interrelated components to this issue: (1) influencing faculty members to improve instructional practices at the course level, and (2) providing evidence to help inform key strategic decisions at the institutional level.
  • Broadly summarized, our results reveal a disparity between the keen interest in research-based evaluation and the level of resources that are dedicated to it—prompting a grass-roots effort to support this work.
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    The SEI program is working with the teaching and learning community to gather evidence of the impact of instructional innovations and current practices and to help evaluate the results. The calls for more accountability in higher education, the shrinking budgets that often force larger class sizes, and the pressures to increase degree-completion rates are all raising the stakes for colleges and universities today, especially with respect to the instructional enterprise. As resources shrink, teaching and learning is becoming the key point of accountability. The evaluation of instructional practice would thus seem to be an obvious response to such pressures, with institutions implementing systematic programs of evaluation in teaching and learning, especially of instructional innovations.
George Bradford

A unified framework for multi-level analysis of distributed learning - 0 views

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    A unified framework for multi-level analysis of distributed learning Full Text: PDF Authors: Daniel Suthers University of Hawaii, Honolulu, HI Devan Rosen School of Communications, Ithaca College, Ithaca, NY Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.
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