data

Early Predictors of Success or Failure

Current retention software solutions send "early alerts" when students don't pass exams or fail classes, but often by that time in a semester or quarter, it's too late.

The data, however, exists to predict issues earlier and craft interventions that can change student outcomes.

Called, engagement, how college students interact with online tools says a lot about how they will do in a class. Data collected by Civitas Learning shows that students who engage early and often with a Learning Management System (LMS), do better in courses. 

That, of course, makes sense. The key insight is those students who don't engage are indicating early on by their non-engagement that they are encountering obstacles. That's where a communication system like, a text messaging platform, can reap the most rewards. 

 

 

Disruption

Consulting:

"It has always involved sending smart outsiders into organizations for a finite period of time and asking them to recommend solutions for the most difficult problems confronting their clients."

But today the consulting industry is undergoing tremendous change.  It's a disruption that other professional services should heed - namely, management and leadership training, and professional development.

What does the disruption look like?  Technology

"One of the most intriguing of these is McKinsey Solutions, software and technology-based analytics and tools that can be embedded at a client, providing ongoing engagement outside the traditional project-based model."

Don't believe me?  See what Clay Christensen has to say about it over at the HBR.

What Language Reveals

Part of my vision of Refleta was to bring in disparate data streams and use them to form a picture of the consumer. Once I realized that device data was difficult to get at (mainly because most of middle America still doesn't own many devices) I turned to social media data. Turns out there's a lot you can glean from a Facebook post or Tweet. To start, my team built a sentiment analysis engine and ran it against the post and tweets data we were granted access to by alpha testers. We were able to discern when testers were feeling sad, happy, angry, etc. even without the specific use of those words.

But that wasn't a novel idea. In fact, there is plenty of research out there that shows the language you use says a lot about you.

And now there's even more interesting work in the space. Check it out.

What Works?

“Personal anecdote trumps data.” F. Joseph Merlino

Unfortunately, that has been the case in terms of measuring the effectiveness of education materials. But today there is What Works, a clearing-house of educational tools ("programs, products, practices and policies") that have been tested using the research methodologies (e.g., double blind clinical trials) usually used in the medical industry.

Interestingly, the non-profit world is also facing a similar dilemma.  Are their efforts effective?  While they are dealing with a lot of push-back, mainly of the "the transformation of lives can't be measure" kind, it would be interesting to see if they will adopt more rigorous testing.  For an interesting story on the challenges faced by non-profits/philanthropy in this regard, see this American Life episode.

Finally, and more important to me, I would like to see soft skill development subjected to the same type of rigor.  How can we help people develop skills like self-awareness and how do we measure whether or not our efforts were effective?  I believe companies are a great starting ground for this type of exploration and "workforce science" seems to be a good start, though less focused right now on soft skills, as opposed to technical training.

 

 

Deep Learning

Google recently introduced a potentially very interesting open source tool called word2vec. It is software designed to understand the relationship between words without human intervention.  How? Through the use of "deep learning" - basically neural network models on steroids.  Essentially these models understand the features of an input (in this case words) and how those inputs relate to each other.

 

You don't have to understand the ins and outs to see how potentially useful software that can do this is.  One potential application is better understanding of tweets.  Typical sentiment analysis, for example, has a difficult time with tweets due to their short nature and the fact that they are often infused with symbols and sarcasm.  With deep learning techniques it may be possible to decipher the sentiment of tweets more accurately.  Can you imagine?

Health Tracking

Aetna is getting ready to release CarePass, an app for accessing your health device data, medical records and health information all in one place. Two interesting things to note from the article:

“We see some more aggressive employers like Safeway, where they are driving outcomes by swabbing the cheeks of employees to see whether they are smoking or not.” U.S. law says that smokers can be charged higher premiums."

Wow - swabbing! And Zeo, the sleep device, is mentioned in the article as a now defunct company.

This area is moving fast. My question - will folks feel comfortable with an insurance company having more their data?

Data and Movies

Even movie scripts can't escape the data analysis trend. Mr. Bruzzese of Motion Picture Group, a former statistics professor, analyzes scripts for movie studios. He relies on statistics and survey results of other movies. His findings show things like bowling scenes are present in movies that flop, so best to avoid them.

He doesn't currently use algorithms but I think it's only a matter of time.

Big Data and Recruiting

Recruiting is using big data to uncover potential talent. But right now it's unclear if all the data currently aggregated can predict whether a candidate will be a cultural fit or work out in the long run.

Whether these algorithms work in the long run (where it's defined as the employee remaining employed by the employer for X years), only time will tell and it should be fairly simple to collect the retention data on employees.

When it comes to cultural fit understanding a candidate's personality is important. I suspect many of these algorithms may not be factoring in data around personality and soft skills. The ethical/moral issues aside, it's possible that sentiment analysis on the prospect's social media communication might give an employer a sense over time of a candidate's emotional stability. Or even an analysis of pronoun usage - studies have shown pronoun usage can highlight depression. But is it fair to do this?

Still, if you break down soft skills to key components like effective communication, leadership, conflict style, etc., I have to imagine there are ways to determine it with data. We reveal so much about ourselves - the independent variables are out there.

What I See

Pulling some of my thoughts together. What I see:

1. The 18 to 25 year-old set. These folks have to either know immediately that they want to be nurses (good economic choice, btw) or auto mechanics (ditto) OR they have to have the grades, focus and resources to go the Stanfords/Dartmouths/Yales of the world. There is a huge gap in the middle that is filled mainly with state schools, online universities (univ of phoenix) or community colleges. All are more and more expensive and often quite directionless.

2. The lack of high school counselors and good ones at that

3. The boom in career/life coaches

4. Reality TV and interactive TV (see Bravo Channel) as a distribution model; it’s still super hard to get anything to scale in the online world

5. The more and more entrepreneurial world – meaning because there are no IBMs anymore, people, in order to survive, have to become entrepreneurs – approach their lives like running a business

6. Self-awareness or lack thereof and the lack of these skills in young people – you seem to learn only if you’re lucky; no educational focus or system around it: Who am I? What am I good at? What can I do with that?

7. Life long learning – how education or taking a class can be viewed as a way into self-awareness and personal growth (even if the class isn’t about self-development)

8. Good teachers – there’s a trend online where more are being highlighted – see MIT Open Courseware Initiative

9. What we say about ourselves implicitly and explicitly – the beauty of online is mostly in the data; a way to use that data in a valuable way to the provider of the data, the consumer

10. Learning on the job – there are teaching hospitals, why not teaching businesses? Educational co-ops

This all amounts to something. I'm working through what.

Charts!

You know how I love data and the presentation of data. So naturally I'm interested in charts! Here are some charting sites I've discovered:

1. Chartio

2. Easel.ly

3. Infogr.am

There's also this great summary site for lots of different data charting and mapping tools. 

Here's a website that's compiled 50 great examples of data visualization: Web Designer Depot. The We Feel Fine project is included and worth checking out. I'm not sure, though, how many of these examples would pass the Tufte test - as in, can you actually learn anything from them. You be the judge.

Have fun!

 

 

Data and Language

A Ted talk by Deb Roy on the birth of a word:

What I'd like to see is the link between media consumption and thoughts, which I suspect affect beliefs. How to get at thoughts? A user's online journal, blog or social media posts.

Like a baby learns to talk, I can imagine a person's belief formations or reinforcement can be tracked. What do you think?

What's Happening in Self-Help

Boris Kachka's article in New York Magazine lacks a thesis except to say self-help has changed and it's now under different guises. It's changed: "The guru has given way to the data set" - meaning more science is involved.

It's everywhere: "books on 'willpower' and 'vulnerability'—self-help masquerading as ‘big-idea’ books.”

Is this a bad thing? He hints that it might be.

"Strains of self-help culture—entrepreneurship, pragmatism, fierce self-­reliance, gauzy spirituality—have been embedded in the national DNA since Poor Richard’s Almanack. But in the past there was always a countervailing force, an American stew of shame and pride and citizenship that kept these impulses walled off, sublimating private anxiety to the demands of an optimistic meritocracy. That force has gradually been weakened by the erosion of all sorts of structures, from the corporate career track to the extended family and the social safety net."

What do you think?