Several years ago I wrote a post titled "Pilots: Too many ed tech innovations stuck in purgatory", where I used Everett Rogers' Diffusion of Innovations framework to explore why we have plenty of pilots but not very many large-scale adoptions of ed tech innovations. What we are seeing in ed tech in most cases, I would argue, is that for institutions the new ideas (applications, products, services) are stuck the Persuasion stage.
Over the past several months, we have worked with our partners at LISTedTECH as they ramp up their efforts to collect data on LMS usage in the K-12 market in the United States. This is a massive effort as the market includes more than 130,000 individual public and private schools, and more than 13,600 school districts, according to recent NCES documentation. We are aware of several private data sources with estimates on the K-12 LMS market, but there are no public sources.
There has been a lot of interest in Tuesday's guest post by Steve Lattanzio from MetaMetrics on an alternate approach to college rankings that relies on algorithmic analysis thousands of variables from the College Scorecard instead of typical cherry-picking of variables and subjective analysis. There have been some good questions posted on social media and blog comments asking for more information on the algorithms or assumptions behind the algorithms.
Muck Rack makes it simple to find people, tweets, or articles that mention any name, keyword, company, hashtag etc. We've compiled this guide to help you make the most of your search.
Selecting a term
Start searching tweets, articles from media outlets, articles mentioned in tweets, journalists'
names, titles and bios with some suggested searches:
Companies or Topics (e.g. iPhone, Microsoft)
Phrases (e.g. "cloud computing") — use quotes to keep the terms together
Twitter handles (e.g. @username) — returns those who have mentioned or replied to
Names (e.g. "David Pogue")
Hashtags (e.g. #sxsw, #london2012)
Bio details (e.g. vegan, Olympics, father)
Muck Rack's Advanced Search allows for many boolean operators.
Find results that mention multiple specified terms, use AND or
+. For example, ensure each result contains both Elon Musk and Mark Zuckerberg by
searching Musk AND Zuckerberg or Musk + Zuckerberg.
Use the operators OR or , to broaden your search when you'd like either of
multiple terms to appear in results. (This is the default behavior of our search when no operators
are used). For example, results will contain either cake or cookie by searching cake OR cookie or cake,cookie
Use NOT or - to subtract results from your search. For
example, searching Disney will yield results about the Walt Disney Company as well as Walt Disney
World Resort. To exclude mentions of Disney World, search for Disney -World or Disney
When using one of these operators with a phrase, enclose it in quotation marks. For example, you can
find results about smartphones excluding Apple's iPhone 4S by searching smartphone -"iPhone
Exact case matching or punctuation
If you're searching for a brand name or keyword that relies on specific punctuation marks or capitalization, you can
find results that match your exact query by adding matchcase: before the keyword you're searching for, like matchcase:E*TRADE .
Use parentheses to separate multiple
boolean phrases. For example, to find journalists talking about having fun in Disney World or
Disneyland, search for ("disney world" OR disneyland) AND fun.
An asterisk can be used to search for any variation of a root word truncated by the asterisk. For example, searching for admin* will return results for administrator, administration, administer, administered, etc.
A near operator is an AND operator where you can control the distance between the words. You can vary the distance the near operation uses by adding a forward slash and number (between 0-99) such as strawberries NEAR/10 "whipped cream", which means the strawberries must exist within 10 words of "whipped cream".