MOOCs should be the Holy Grail of student data, but they aren't there yet.
One of the great promises of massive open online courses, besides making education more accessible for more students, is the treasure trove of student data collected on a grand scale.
Large amounts of data are exactly what higher education needs to stay relevant in this era of disruptive change, as Arizona State University's Adrian Sannier pointed out in his keynote at last year's Campus Technology annual conference. The only way to make sure colleges and universities are continually boosting student success, he said, is evidence-based pedagogy. And that requires scale: "You can't take evidence one class at a time, one person at a time — it takes too long, you don't get a broad enough sample…. I'm not sure you can do it at a university, at a single institution. You may not have enough scale, you may not have enough size."
American schools are in crisis. Thirty years after the scathing education report "A Nation at Risk: The Imperative for Educational Reform" they continue to lag behind in reading, science, math and high school graduation rates.
While written and oral language dominate instruction, the explosion of visual information has created new opportunities to represent complexity, reveal themes, explore data, and communicate information in powerful ways. Here is an overview of some of my favorite examples of visual data representation for education.
We invite you to read our latest SVC2UK White Paper, “The Future of E-ducation“, written in collaboration with Gold Mercury International, the Corporate Vision® Strategy Think Tank. The Paper draws on many of the case studies from SVC2UK 2013 and explores what the future is likely to look like for teachers and students.
"Register now for Tackling the Challenges of Big Data, an online MIT course for engineering and business professionals offered by MIT Professional Education and CSAIL.
This Online X course will survey state-of-the-art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications.
Each module will introduce broad concepts as well as provide the most recent developments in research."
At a conference devoted to exploring Big Data in higher education, the event’s keynote speaker had a surprisingly contrarian take on the subject.
“Big Data is bull—,” Harper Reed, the chief technology officer of President Obama’s 2012 campaign, said to an audience that included many campus IT officials hoping to learn more about Big Data’s benefits.
Reed, who used the power of data to help Obama secure reelection, said the term is just a marketing tool meant to drive college and university IT officials toward expensive technologies for storing and analyzing data.
Kim Flintoff's insight:
Is Big Data just too big to distil into meaningful decisions?
No two students are the same; each brings their own interests, learning modalities, strengths, and prior knowledge of the topic at hand. Addressing students’ individual learning needs in the classroom has always been a key challenge for educators—but new instructional technologies can help.
With student polling software, for instance, teachers can get an accurate, immediate view of each learner’s progress—a concept known as “feedback for learning”—and can tailor their instruction to meet students’ individual needs in real time. And putting mobile technology in the hands of every child allows students to explore their own interests and opens a whole world of self-guided instruction.
With the generous support of Promethean, we’ve assembled this collection of stories to help you use technology to personalize learning for students in your own schools.
Kim Flintoff's insight:
Personalisation is one of the big hopes for the "big data:" movement ion Higher Education.
The collection of data in higher education will never produce a single formula for success, no matter how much students clamor for such a miracle algorithm.
That, along with other harsh realities of data collection and analysis on college campuses, was discussed during the “Allure of Big Data” session at the EDUCAUSE 2013 conference in Anaheim, Calif., where campus technologists from across the world gathered to discuss trends in educational technology.
When education investors talk about so-called adaptive learning, in which a computer tailors instructional software personally for each student, the name Knewton invariably surfaces. The ed-tech start up began five years ago as an online test prep service. But it transformed the personalization technology it uses for test prep classes into a “recommendations” engine that any software publisher or educational institution can use. Today the New York City company boasts it can teach you almost any subject better and faster than a traditional class can. At the end of 2012, 500,000 students were using its platform. By the end of this year, the company estimates it will be more than 5 million. By next year, 15 million students. Most users will be unaware that Knewton’s big data machine is the hidden engine inside the online courses provided by Pearson or Houghton Mifflin Harcourt or directly by a school, such as Arizona State University and University of Alabama.
The Hechinger Report talked with David Kuntz, Knewton’s vice president of research, to understand how the company’s adaptive learning system works. Kuntz hails from the testing industry. He previously worked at Education Testing Service (ETS), which makes the GRE and administers the SAT for The College Board. Before that Kuntz worked for the company that makes the LSAT law school exam.
"Education is increasingly occurring online or in educational software, resulting in an explosion of data that can be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data."