More and more students are working with digital learning material. The data that’s being generated by student interaction with this material is the fuel for the Learning Analytics engine. Analyses of this data can help to create a clearer picture of the progress a student is making, the level he or she is working on, and the way students prefer to learn. Kennisnet’s The Promise of Learning Analytics Infographic you will review the road to more differentiated and personalized education. Want more? Check out 5 Reasons Why Learning Analytics are Important for eLearning, that highlights some of the most significant arguments for why Learning analytics have the power to improve eLearning in education and training.
Reposted from the Wall Street Journal:
Interest in specialized, one-year masters programs in business analytics, the discipline of using data to explore and solve business problems, has increased lately, prompting at least five business schools to roll out stand-alone programs in the past two years. The growing interest in analytics comes amid a broader shift in students’ ambitions. No longer content with jobs at big financial and consulting firms, the most plum jobs for B-school grads are now in technology or in roles that combine business skills with data acumen, say school administrators.
Amy Hillman, dean at Arizona State University’s W.P. Carey School of Business, said interest in a year-old master’s program in business analytics has spread “like wildfire.” More than 300 people applied for 87 spots in this year’s class, according to the school. Ayushi Agrawal, a current Carey student, said she left her job as a senior business analyst at a Bangalore, India, branch of a Chicago-based analytics firm to enroll in the program. As data become central to more business decisions, “I want to be at the forefront” of the emerging field, the 24-year-old student said.
Michael Rappa, founding director of the Institute for Advanced Analytics at North Carolina State University, said analytics is best studied in an interdisciplinary context, rather than only through a university’s business school. “Analytics programs in a business school will always be in the shadow of the M.B.A. program,” said Dr. Rappa, architect of the Institute’s popular Master of Science in Analytics program, launched in 2007. “That’s how the school is ranked.”
Reposted from NPRed:
“When students at Purdue University are reading their homework assignments, sometimes the assignments are reading them too. A software program called Course Signals tracks various pieces of information, including the number of points earned in the course and the amount of time the student has spent logged in to the college’s software platform. Course Signals combines this data with knowledge about the student’s background, such as her high school GPA, and generates a “green,” “yellow,” or “red” light representing her chances of doing well in the course. Professors then have the option of sending students text messages or emails either warning them to buckle down or cheering them on.
This is one early real-world application of the new and rapidly expanding fields of research called learning analytics and educational data mining. When students use software as part of the learning process, whether in online or blended courses or doing their own research, they generate massive amounts of data. Scholars are running large-scale experiments using this data to improve teaching; to help students stay motivated and succeed in college; and even to learn more about the brain and the process of learning itself. But with all this potential comes serious concerns. Facebook caused a furor over the past couple of weeks when the company’s lead scientist published a research paper indicating that the social network had tinkered with the news feeds of hundreds of thousands of people in an experiment to see whether their emotions could be influenced. As unsettling as that may have been, users of a recreational social network are free to click away or delete their accounts at any time. College students, on the other hand, are committed. Earning a degree is crucial to their future success, and requires a significant investment of time and money.
The field of learning analytics isn’t just about advancing the understanding of learning. It’s also being applied in efforts to try to influence and predict student behavior. It’s here that the ethical rubber really meets the road. With the Course Signals project, for example, an algorithm flags a certain group of students as being likely to struggle. The information it draws on includes a demographic profile of the student: his or her age, whether they live on campus, and how many credits they’ve attempted or already earned in college. Depending on the way that prediction is communicated to teachers and students, it could have troubling implications if the predictions unduly influence teachers’ perceptions of their students.”
In Google Analytics’ “Sharpening Your Skills for the Data-Driven Age” infographic, the case is made for data fluency and its associated skills and the data to back it up. Links to vital resources to dig deeper are included and worth perusing.