Translate to multiple languages

Subscribe to my Email updates
Enjoy what you've read, make sure you subscribe to my Email Updates

Thursday, January 19, 2017

Everything Depends on the Data | EDUCAUSE Review

Key Takeaways
  • Trends in education promise to improve how institutions support students by providing the student, instructor, or institution the ability to make more informed decisions using student-created data.
  • Unfortunately, as our reliance on data increases, our ability — especially students' ability — to access the data seems to diminish.
  • Whereas learning analytics gives a small number of users access to a single large data set, personalized learning requires that a large number of users — students — have access to their own relatively small portion of data within various systems.
  • While no solutions come without a cost, enormous potential benefits to institutions, educators, and students arise if we adopt systems that provide access to data produced by students.
Photo: Todd Bryant

Here's another interesting article from EDUCAUSE Review, published by Todd Bryant, Language Technology Specialist below.

The rise of big data accompanies a diverse number of trends in education that promise to improve how our institutions support students. While these trends may vary in scope, they all provide the student, instructor, or institution the ability to make more informed decisions. To do so, they rely on our ability to access and synthesize student-created data. This includes structured data such as attendance, completion rates, and grades, along with the unstructured data of student assignments, discussion posts, and any content created by students. I therefore find it concerning that as our reliance on data increases, our ability to access the data seems to be diminishing.

Learning Analytics and Personalized Learning 
When it comes to the intersection of data and education, most of us think first of learning analytics. The data for learning analytics relies largely on structured data mined from the institutional learning management system (LMS). Currently it primarily provides educators with early warnings of students who might be falling behind. While these data points might seem rather simple, putting them together and in the hand of educators has shown significant results in terms of student retention and graduation rates (for an example, see figure 1 from Valdosta State University1).2 In the future, improvements in teaching could go even further by finding specific moments of success and failure within a course. To do so requires a more detailed view of a course that would allow us to see when students engage in a discussion, where they become stuck or disinterested in a topic, and which activities result in meeting their learning goals. This in turn requires a more detailed and less structured data set, including the clickstream within the LMS, discussion board posts, and essays.3...

Artificial Intelligence 
Artificial intelligence also shows promise as a way for students to receive feedback outside of class. Current examples of artificial intelligence–generated feedback in education are limited to simple questions using information found in the syllabus or feedback from multiple choice questions.4 However, given the capabilities AI has shown in other areas, in the future we can expect helpful AI assistants for students outside of regular class time. In some areas, it's already here. DuoLingo recently released a chat bot capable of carrying on simple conversations in Spanish, French, and German for foreign language learners.5 These bots should get better with time as they learn from users pushing for more complicated discussion. Another possibility not far off is for bots to lead asynchronous discussions outside of class. Google is already working on a conversation AI to limit the worst aspects of online discussion by recognizing conflict and abuse.6 A bot designed for education could lead a discussion if it could complete tasks such as starting with open-ended questions to guide the discussion, recognize themes or arguments presented, and then offer counterarguments. An AI can learn these tasks, but only if it has data from which to model, in this case a discussion led by an instructor.7
Read more... 

Source: EDUCAUSE Review