Learning & Leading Through Technology

STANDARDS ALIGNMENT

QUALITY/
APPROPRIATENESS

Internet Search 2.0

Publication of the LRMI metadata tagging standard will allow content creators to assign education-specific context when they upload new content to the internet. This will let search engines and other nonhumans “know” things about webpages that they couldn’t before. For example, they can identify the subject area the content covers, the age range of the intended audience, the standards or learning objectives the content is intended to address, and so on. This extra information allows search engines, portals, and other technologies to help educators and students find precisely the resources they need at exactly the time they need them—which, after all, is one goal at the heart of personalized learning.

As more publishers tag their resources to the LRMI specification and as the major search engines incorporate the LRMI markup, tagged materials should become more discoverable for online searchers.

How would it work? Try it for yourself: Search Google for potato salad, and you’ll notice you can quickly narrow the million results yielded by using the More tab listed below the search box. Select Recipes and then Search Tools to narrow your search based on ingredients, cook time, and calories. Many shopping websites, such as Amazon.com, provide similar opportunities for conducting focused searches. The LRMI will do the same thing, only with educational filters.

By allowing educators to conduct more targeted searches and more easily find resources to meet their students’ learning needs, the LRMI can play a key role in enabling personalized instruction. In this way, it serves as an important component of the broader mission to use “big data” to improve educational opportunities for all students.

TOPIC/
CATEGORY

RESOURCE
USE/CONTEXT

Publication of the LRMI metadata tagging
standards will allow content creators to assign
education-specific context when they upload
new content to the internet.

Enter Big Data

So far, most of the media focus on big data has been on the collection of student information for assessment and to pinpoint academic strengths and weaknesses. For instance, inBloom ( www.inbloom.org) has piloted a shared technology infrastructure designed to help states and districts provide teachers with the instructional data and tools they need to best meet students’ individual needs and learning styles. To accomplish this, inBloom has built a set of shared services that will connect disparate student data and learning content that exist in different formats and locations. The goal is to centralize student data and make it more manageable. This frees up teachers’ time to do what they do best: teach. Once teachers target the types of instructional support that students need, the LRMI can help them find the appropriate resources.

How? Let’s revisit our original example. Data available through inBloom would enable Thompson to know not only that Sammy struggles with multiplying fractions, but also that he learns best by watching videos and then taking multiple-choice quizzes. As noted earlier, however, there are more than 4. 1 million instructional resources related to the topic of multiplying fractions. How can Thompson find exactly the right one to help Sammy?