Data–including big data and learning analytics–has incredible potential for teaching and learning
The term “big data” is everywhere and, in a nutshell, is the term used today to describe large collections of data that companies use to personalize their products and services.
But what does this concept mean for K-12 education? Well, for starters, increased knowledge of individual students can lead to personalized teaching and learning. This is called learning analytics, which involves using big data for educational purposes, as defined by the New Media Consortium K-12 Horizon Report. The report is produced in collaboration with the Consortium for School Newtworking (CoSN), which recently released a report that examines, in-depth, learning analytics’ potential.
School districts already use data, but learning analytics would enable educators to use this data to a greater extent, examining what the report calls “student-level data” and using that information to determine how students are learning, what might help them learn better, and what teacher practices are or are not having an impact on this learning.
(Next page: The 6 questions you should ask about data)
The U.S. Department of Education has outlined three different ways to organize data:
- Traditional metadata helps users find and sort online content, but sheds little light on how that content is used
- Activity streams deal with user activity and what is done with an online resource
- Paradata builds on activity streams and describes how multiple people use an online resource or the multiple actions of a single person.
It is this paradata, or usage data, that makes learning analytics possible, the report notes.
“When paired with assessment performance data, paradata can be used to map relationships among usage of online resources, learning and achievement–and deliver more customized recommendations to students for next steps in learning based on their study habits and interests,” according to the report, which quotes from New Media Consortium research.
The report, available to CoSN members [1], explores learning analytics’ potential in great depth, and notes that challenges still remain for districts:
- “Data analysis software and technology approaches to data capture and synthesis are still under development and likely will change and improve”
- “The consistency and dependability of data is a real issue, especially with ‘big data’: Is it accurate? Is it complete?”
If educators want learning analytics to have a positive impact, they should ask a few questions:
1. Which instructional resources do students access most during a defined timeframe (e.g., a grading period)? How and why are they useful?
2. How are students who regularly access learning resources doing academically? How does this compare to students who rarely access learning resources?
3. Which standards are high-performing, on target and under-performing students meeting? Which standards are they struggling with? What learning resources can we recommend for specific under-performing students
Students, too, should ask questions:
4. Am I making progress and meeting expectations in my learning?
5. Which standards have I mastered? Which ones am I not on target to master?
6. What more do I need to do, and which learning resources are recommended, to help me meet learning targets?