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Apollo Group and Carnegie Learning. In August 2011, the Apollo Group, which runs the University of Phoenix, bought Carnegie Learning, which develops interactive adaptive learning software for math instruction, for $75 million. “Founded by cognitive and computer scientists from Carnegie Mellon University in conjunction with veteran mathematics teachers,” Carnegie’s website declares, “Carnegie Learning has the courage to not only question the traditional way of teaching math, but re-invent it.”
Carnegie Learning entered the higher education market in 2007 after working primarily in middle school and secondary school markets. The company thus has a deep reservoir of content and a decade of experience in developing adaptive learning systems. With its acquisition, Apollo further extends its personalized instruction platform to a broader post-secondary student audience. Apollo hired Mike White away from Yahoo to serve as the Chief Technology Officer for the re-organized Carnegie Learning and assigned more than one hundred technologists to the project. According to White, Apollo sees “adaptive learning as the future. It is about individual learning outcomes.”
Pearson and Learning Catalytics. In Spring 2013, Pearson, which has spent more than $1 billion on education companies since 2011 in an effort to extend its reach beyond textbooks and other publications, acquired Learning Catalytics, a learning assessment system created by Harvard University educators Eric Mazur, Brian Lukoff, and Gary King.
The Learning Catalytics system grew out of Mazur’s persistent efforts to perfect interactive teaching. That effort is documented in Peer Instruction: A User’s Manual, which outlines the Peer Instruction method he began developing in the early 1990s and which helped fuel the development and adoption of “classroom clicker” technology. Learning Catalytics’ cloud-based software system builds on the clicker model to mine data so as to better “engage students by creating open-ended questions that ask for numerical, algebraic, textual, or graphical responses—or just plain multiple-choice,” in the words of the company’s website. Instructors use data from the system to send peer interaction directions to students. “Students use any modern web-enabled device they already have—laptop, smartphone, or tablet,” and the system mines data generated by their responses to open-ended questions to direct them to peers for interaction and debate.
In “Colleges Mine Data to Tailor Students’ Experience” (The Chronicle of Higher Education, December 11, 2011), Marc Parry describes how the system is used to direct peer instruction activities in Brian Lukoff’s Harvard calculus class: “The software records Ben Falloon’s location in the back row and how he answers each practice problem. Come discussion time, it tries to stir up debate by matching students who gave different responses to the most recent question. For Mr. Falloon, the system spits out this prompt: Please discuss your response with Alexis Smith (in front of you) and Emily Kraemer (to your left).”
Instructors receive graphical displays of the responses, recommendations, and results of the interactions. “Advised by the system to interact, they engage in a debate which is the point which gets them arguing—exactly what the matchmaking algorithm intended. Meanwhile, Mr. Lukoff’s screen displays a map of how everyone answered the question, data he can use to eavesdrop on specific conversations.”
Skeptics, who believe that simply monitoring and cataloging data responses to classroom questions minimizes or even eliminates creativity in the learning environment, consider the modifying of college-level teaching and learning through the use of analytics akin to employing standardized testing in primary and secondary education—with predictable and similar results. Mazur counters that learning analytics systems solve three problems faced by faculty in the contemporary classroom: “One, it selects student discussion groups. Two, it helps instructors manage the pace of classes by automatically figuring out how long to leave questions open so the vast majority of students will have enough time. And three, it pushes beyond the multiple-choice problems typically used with clickers, inviting students to submit open-ended responses, like sketching a function with a mouse or with their finger on the screen of an iPad.”
Michael Horner, co-founder and executive director of the Christensen Institute, argues that the ability to harness data generated by analytics frees instructors to focus on working directly with students. Ravishanker also notes in his paper that capturing data about learning activities both in class and online provides faculty and provosts a wealth of information to help evaluate systems currently in place. Horner and others go farther, arguing that analytics will help higher education move away from a factory model of education toward a learning-focused model.
As corporate entities like Pearson partner with the academy, it will be important to review these developments with a critical eye on how they cohere with the strategic needs of your institution. It will be difficult to dismiss out of hand resources that make your own data so readily available, possibly enabling informed development of learning platforms that make sense for this century.
Desire2Learn. Founded in 1999 by John Baker, Desire2Learn provides cloud-based learning management systems for higher education. In 2012, Desire2Learn entered the learning analytics arms race in earnest with $80 million in financing from New Enterprise Associates and OMERS Ventures. According to an NEA press release, the company is focused on “transforming the way the world learns in a rapidly growing market fueled by the adoption of online and mobile learning tools, digital textbook distribution, and advanced learning analytics.”
Desire2Learn has been building toward adding learning analytics to its platform for some time, having developed a team to build a framework of algorithms and predictive models to analyze student learning. The team developed a “risk quadrant” that provides weekly predictive representations of individual learners’ progress in a given course. Quadrants display students who are fully engaged and on track towards passing; students who are less engaged but still on track; students who are in danger of withdrawing from the course; and students who are in danger of failing or receiving a poor grade.
The tool begins making data-driven predictions on the first day of a course. Interviewed by Ellis Booker for Information Week (“Can Big Data Analytics Boost Graduation Rates?” February 5, 2013), Baker brashly described how dynamic student data, meshed with available historical course data, provides a data framework allowing the system to make predictions of student learning performance with 95 percent accuracy as early as weeks two and three. Desire2Learn launched the learning analytics product as part of an integrated suite of resources called Desire2Learn Insights, which the company says can deliver high-performance reports, data visualizations, and predictive analytics to help institutions measure the success of their overall learning environment.
Desire2Learn has taken the analytics toolkit for the classroom that providers like Learning Catalytics have deployed, and implemented it in a cloud-based, intentionally online learning platform wrapped within a familiar learning management system. This has interesting implications for campuses making decisions about MOOCs and other forms of online education.
 Josh Keller, “Apollo to Buy Adaptive-Learning Company for $75-Million,” The Chronicle of Higher Education, The Wired Campus, August 2, 2011, http://chronicle.com/blogs/wiredcampus/apollo-to-buy-adaptive-learning-company-for-75-million/32658.
 Marc Parry, “Colleges Mine Data to Tailor Students’ Experience,” The Chronicle of Higher Education, December 11, 2011, http://chronicle.com/article/A-Moneyball-Approach-to/130062/.
 “Desire2Learn Raises $80 Million in Financing Round from NEA and OMERS Ventures,” accessed August 5, 2013, http://www.desire2learn.com/news/2012%2FDesire2Learn-Raises-80-Million-in-Financing-Round-Led-by-NEA-and-OMERS-Ventures%2F.
 Ellis Booker, “Can Big Data Analytics Boost Graduation Rates?,” Information Week, February 5, 2013, http://www.informationweek.com/big-data/news/big-data-analytics/can-big-data-analytics-boost-graduation-rates/240147807.