Are You Ready for Learning Analytics?


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.”

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