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Where Instructional Design Meets Big Data
By Reuben Tozman
“We didn’t cause the business to achieve its objectives, but our data supports the system for helping the business achieve its objectives. You can do this by designing your interventions to work within the system (I’m not talking LMS or any technology for that matter) and generating data that’s important for the business.”
The business environment, learner profiles, training environment, and IT infrastructure are all things that instructional designers consider in their design plans. For many in the instructional design space, the term big data is something that is probably neither interesting nor relevant to the craft of design.
In the coursework leading to a master’s in educational technology, any discussion about using data to inform the design process is generally tied to creating courses that improve test scores. There is nothing about designing experiences to generate a certain and specific type of data.
Where does big data fit in?
College Degrees, Designed by the Numbers
By Marc Parry
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.
With 72,000 students, Arizona State is both the country’s largest public university and a hotbed of data-driven experiments. One core effort is a degree-monitoring system that keeps tabs on how students are doing in their majors. Stray off-course and you may have to switch fields.
Visit: http://chronicle.com/article/College-Degrees-Designed-by/132945/
The Rise of Big Data
By Louis Soares
Technology will transform higher education, just as it has many other industries. This will happen as information technology becomes embedded in more institutional processes, such as course enrollment, classroom instruction, and student services. In other industries, including healthcare and travel, this embedding of technology is generating what is being called “big data.” Big data is fine-grained information—about customer experiences, organizational processes, and emergent trends—that is generated as customers conduct normal business. The organization of this data can be a rich source of business analysis that improves performance and even points to new opportunities.
Visit: http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume47/TheRiseofBigData/250729
Quest for College Accountability Produces Demand for Yet More Student Data
By Paul Basken
Despite growing pressure from policy makers and prospective students for colleges to prove their value, the institutions have often insisted that their unique missions make simple measurements forbiddingly difficult.
Now they have documented proof.
After three years of studying ideas for measuring institutional quality, an expert panel assembled by the National Research Council delivered a 192-page report on Thurday that indicates just how hard it is to do that.
Visit: http://chronicle.com/article/Quest-for-College/131910/
Read the Report: http://www.nap.edu/catalog.php?record_id=13417
Systemic Mendacity
By John V. Lombardi
Whether it goes by the name of exaggeration, half-truth, misrepresentation, distortion, or dissembling, lying is endemic in all of education. Lies vary in intent and magnitude — but they tend to escalate under pressures of accountability. Over the past few years, state government efforts to link K-12 school funding and teacher and administrator tenure with student progress have led to widespread data falsifications. In at least half of the states, K-12 teachers and administrators have made test questions available in advance or changed students’ incorrect responses. This form of cheating is predictable: The more that data are used in decision-making — especially fiscal allocations — the more they will be subject to corruption.
Continued at: http://www.insidehighered.com/blogs/reality-check/systemic-mendacity#ixzz1pftD6Qlk
College Completion: Who Graduates from College, Who Doesn’t, and Why It Matters
From the Chronicle of Higher Education
College Completion is a microsite produced by The Chronicle of Higher Education with support from the Bill & Melinda Gates Foundation. Its goal is to share data on completion rates in American higher education in a visually stimulating way. Our hope is that, as you browse around the site, you will find your own stories in the statistics and use the tools we provide to download data files; share charts through your own presentations; and comment, start conversations, or provide tips about this important topic.
This microsite is a tool to help you navigate a complex subject: which colleges do the best job of graduating their students. You can also benchmark institutions against their peers and find all the numbers you need to figure out why some colleges succeed while others fail. The site also offers plenty of links to resources for more information, as well as past and current news coverage of this topic.
Continued at: http://collegecompletion.chronicle.com/
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Students Who Don’t Count
By Sara Lipka
The people we tend to call “traditional” students finish high school, march to college, and keep at it until they graduate, more or less on schedule.
National data-collection systems are set up to track the progress of those people: first-time, full-time students who enroll in the fall and get degrees from the places they started, in at most three years for an associate degree or six for a bachelor’s.
But the traditional road is less and less traveled. Of the five million students who started college in the fall of 2009, 2.4 million of them didn’t fit the federal definition, according to the U.S. Education Department.
Nearly 40 percent of all students in college then were enrolled part time, the department’s data show. And many students from that entering class have probably since transferred. According to the National Student Clearinghouse Research Center, a third of students who started college in the fall of 2006 transferred at least once in the five years that followed. At the same time, colleges increasingly serve adult students who may have earned some credit in the past and now want to finish a degree.
Transfers, part-timers, and students who take a break and re-enroll either later or elsewhere—even if they graduate—don’t count.
Continued at: http://chronicle.com/article/Students-Who-Dont-Count/131050/
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Five data-informed steps to optimizing college student retention
By Tim Culver
How can your college or university raise student retention rates? Tim Culver explains how data-informed planning and action can help you meet institutional goals. The University of Texas made news this week as it unveiled an ambitious plan to raise its four-year graduation rate by 20 points for its next class of entering freshmen.
It’s a strong move and in my opinion a correct one. My colleague Jim Hundrieser has written before about why raising standards for student completion would benefit both institutions and students. In their rationale for pushing this change, Texas cited lowering the overall cost of attending college for students (and lowering potential debt loads) as well as using institutional resources more efficiently. Graduating more students in four years requires less time and money for the institution, which in turn frees up more resources to keep graduating students in a more timely fashion.
It’s an ambitious plan and I hope that Texas succeeds. At the same time, having helped many campuses with the same ambition, it’s also quite a challenge. Where do you begin? The answer is data. Simply put, data are the lifeblood to successful student recruitment and retention. You cannot possibly hope to maximize enrollment yields and student completion rates without strong data analysis and planning.
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- 2011 Student Retention Practices Report (hollymccracken.wordpress.com)
How can you turn college student satisfaction data into action planning?
By Julie Bryant
The 2011 Student Retention Practices and Strategies Report indicates that while a large percentage of four-year public, four-year private, and two-year institutions are using satisfaction assessments to make changes to minimize attrition, a smaller percentage of these institutions feel that they are effective with actively using the data.
The struggle to effectively utilize the data on campuses is not uncommon. I have worked with institutions that are very diligent about surveying annually or every other year but do not use their assessment data as effectively as they would like. Furthermore, we have found that satisfaction actually may go down if you keep asking how satisfied students are but do nothing to respond to their feedback. “Data on the shelf” (or on your computer) have no power. The power comes when you use data to make improvements.
Read the Report: https://www.noellevitz.com/papers-research-higher-education/2011/2011-student-retention-practices-report
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Actionable Analytics
By Kenneth C. Green
It’s official: we now live in “the Age of Big Data.” So proclaimed the lead article in the Sunday Review section of the NY Times this past week (12 Feb 2012). Noting “a drift towards data-driven discovery and decision-making,” the Times quotes Gary King, director of Harvard’s Institute for Quantitative Social Science to set the context: “We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.”
The good news for academe is that the “Age of Big Data” means rising demand for training and certification. A 2011 McKinsey Global Institute report estimates that the “big data” initiatives of US firms will require 140,000-190,000 knowledge workers with “deep analytical” skills plus some 1.5 million “data literate” managers. (For comparison purposes, in fall 2008 some 196,000 students were enrolled in graduate programs in engineering, physical sciences, and mathematics; US colleges and universities awarded 1.6 million bachelors degrees in A/Y 2008-09. Source: US Dept. of Education, Digest of Education Statistics 2010, Chapter 3.) The demand for big data skills could be a catalyst for the launch of dozens (hundreds?) of new courses, and as well as new certificate and degree programs at colleges and universities across the country. (The McKinsey report, Big Data: The Next Frontier for Innovation, Competition, and Productivity, is available in PDF, Kindle, and ePub formats from the McKinsey web site.)
Continued at: http://www.insidehighered.com/blogs/digital-tweed/actionable-analytics#ixzz1mYrwCgtA
Read the Report: http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation
Focus on Adult Learning . . . Innovation through Inquiry 