Does our Education System Benefit from Data-driven Decision Making?

THE Journal cover story on

THE Journal cover story on “The Power of Small Data”

Big data is everywhere. You may not expect it to impact how decisions are made in a kindergarten classroom, but big data and analytics are becoming more deeply engrained in the education system – even at the youngest grade levels

A recent article in THE Journal on “The Power of Small Data” asserts that in order to deliver personalized education, districts have to gather and share students’ statistics. The article goes on to explain how the strategic use of data can boost teaching and learning.

The author was kind enough to reach out to me to provide some context for this article. My dissertation for my doctorate in work-based learning at the University of Pennsylvania focused on “Analytics by Degree: The Dilemmas of Big Data Analytics in Lasting University/Corporate Partnerships.” I was able to share my insights in higher education, and also discuss how big data has a major impact on school systems at every level.

Here are some excerpts from the article, along with some of my additional thoughts and research on the topic:

I am seeing a renaissance in data collection in the education field, supported by new tools and technologies. “Recent technologies like big data, the Internet of Things, mobile apps and improved storage have made it possible to acquire, combine, store, analyze, interpret and report findings during any phase of data management.”

“The data repositories residing in disconnected, fragmented departments with little sharing have now been transformed into centralized, interrelated data systems to enable fast and efficient retrieval of interrelated data for quick and informed decision-making.”

However, leadership, based solely on data, will not be successful. Educational systems need to involve key players in the community, particularly the educators themselves, to listen and ascertain what is needed most.

As the article points out, analytics can have many positive uses in the school system, including:

  • Instant Feedback for Students and Parents
  • Formative Assessments that Help Students Grow
  • Using Data to Connect people

However, sadly sometimes educators are spending so much time gathering data (and posting it) that there is little time for instruction. We need to think of how technology, data collection and the onus on high test scores impacts our next generation.

“Data is not the problem. The problem is getting the data to the right people so it can be used…. Data only tells part of the story, and a lot of it is basic so it doesn’t provide the insights that teachers and schools need to pinpoint teaching and learning problems and identify the best ways to solve them.”

We need to ensure that the educators are involved in determining what is needed in terms of useful data, and that a balance is achieved between gathering and analyzing data and effectively teaching our students.

I encourage you to read the full article to learn more.

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Moving the Employment Line: How State Job Centers May Fuel T-Shaped Skills

Often, the best ideas are so good because they have more than just one application. Take T-shaped training, for example.  The concept of the T-shaped employee is helping companies to maximize the potential of their workers.  Could T-shaped training also help today’s unemployment picture? Some states seem to think so, and are taking steps to include it in their employment programs.

For some time, many of us in high tech management have seen the ways that workers have used T-shaping to flourish in the workplace, and it has helped guide our management decisions about hiring and training. Universities have a mixed record when it comes to preparing tomorrow’s workforce for the Big Data future, but “T-shaping” is increasingly viewed as an educational priority. In my view, the development of T-shaped skills may be equally important for the under and unemployed workers of today.

Despite ourmen-in-bread-line stubbornly high national unemployment rate of over 7 percent, The U.S. Bureau of Labor points to a major problem in finding workers who are skilled in analytics. Even with so many people out of work, there are some 3.7 million unfilled jobs in our economy right now that relate to the new Big Data reality.  And as Baby Boomers retire, the skills gap is likely going to get worse. After all, the average age of the “ideal” high skilled worker is now 56 years of age, according to one estimate.  T-shaped employees are clearly becoming part of  employer expectations.

An industrial employer in Milwaukee described the skills-gap problem his company is facing to a reporter, noting that computer skills were needed to run his mill’s equipment. Out of more than one thousand applications, he found only 25 that had the required computer and data skills. A year later, the employer said, he had laid off or lost 15 of those workers.

If given the chance to hone their T-shaped skills, older unemployed workers may be part of the answer.

T-shaped Skills as a Competitive Advantage for States and Regions

What, if anything, are state unemployment training programs doing to fill the the need for T-shaped workers?  It turns out that most states aren’t doing anything, but there are some exceptions. Both New York State and California have specifically adopted the use of T-shaped training (with a focus on such qualities as emotional intelligence), while several other states are more quietly embracing T-shaped training for unemployed workers.

New York and California have two very different ways of approaching T-shaped employee training. New York relies on a more traditional model, using workshops to teach large groups of potential workers at once, while California uses a unique, one-on-one mentoring approach.

Some of the key beneficiaries of T-shaped training efforts in New York, California and elsewhere have been state community college systems. As a result of a demand for high tech-trained workers, community and technical skills colleges have their highest levels of enrollments since World War II.

It is too early to tell if T-shaped training is effective for all or even most of the people and managers who rely on state employment programs for personnel. The rise in part-time employment is especially pronounced among graduates of state employment programs, and some studies suggest T-shaped training is effective in very specific settings, industries, or usually work best only for full-time work.

However, one thing is for certain: corporations are demanding skilled, T-shaped workers. And if state employment programs are unable to provide them, hirers will look elsewhere, or develop these training programs themselves.

 

The Form and Function of Being “Designing” Women

Footpath-Landscapes-Forests-1-1024x768Assuming that form does in fact follow function, the expanding number of women engineers is nearly certain to change the way that engineering operates. Some engineering challenges actually employ the idea of letting what I’m calling the “feminine presence” emerge naturally. When the University of Oregon changed its main pedestrian walkways in the 1960s, the designers hit on a unique approach to measure human presence. Instead of laying the usual brick footpaths and expecting people to “stay on the trail,” they planted grass and watched, for a year, where people walked. Sure enough, and often in defiance of what the original designs organically suggested, people “designed” foot paths and trails though the grass. Then, the engineers laid brick footpaths over the then worn footpaths.

Take, as another example, the 1893 Chicago World’s Fair. In many ways, the event became a watershed for what would become the “American Century” of engineering. The chief board of engineers did not have a single person of color or any women on it. It would be easy to view this as a prime example of how women were left out of the American engineering revolution. However, when we examine the presence of the few women engineers and designers at the event (instead of focusing on the scarcity of women at the event), we are able to see their collective influence and abilities. Notably, as the epic Chicago World’s Fair inched closer to opening day, only the “Women’s Exhibit,” staffed and creatively designed largely by women, was completed on time. In this example, form (i.e., a team of women working together) achieved a successful function.

In an earlier post, I mentioned legislative tools aimed at forcing the employment of women in top management. Please, don’t misconstrue what I’m saying.  Laws can, and should, address the quantitative problem of discrimination, promotions based on the “good ole boys’ network” rather than merit, for example.  In the 1980s, the earliest part of my career, I was one of a very few women who worked in the engineering field. By 1998, the number of women in this field surged to over six percent of all Fortune 500 senior positions. Yet, between 2002 and 2012 – a full decade –  the rate of increase in women executives nearly flat-lined, growing from 14 percent to 18 percent, according to demographic data. This occurred even though the participation of women in the workforce approached full parity with the participation of men in the workforce.

We fix problems only when we can deeply understand them.  Women must search for the reasons why historic predictions of equality in engineering, science, and leadership haven’t worked out as predicted.  The underlying (and I think largely incorrect) assumptions about the “roles” of big data professionals – as defined by men – threaten to overshadow the organic development of the field. Unlike the University of Oregon’s footpaths, women are still typically required to conform to following the brick paths laid by men, cutting themselves off from their natural creative, collaborative and problem-solving abilities.  Big Data is fundamentally a creative, collaborative, problem-solving enterprise: asking questions, seeking answers, communicating results, looking at the bigger picture.   To fully tap into its potential, we need to let go of pre-conceived notions of what “should” be and explore what really works, for women and men alike.

Examining Problems to Uncover Opportunities with Big Data

Realistic vector magnifying glassFailing to fully analyze and understand a problem – to see all of its sides and angles – can prevent us from uncovering an opportunity. In World War II, the Allies believed that they needed to improve their ability to strike deep into Nazi territory without experience heavy losses of their bombers. It was thought that years and countless allied lives could be saved if only the problem of heavy losses from enemy anti-aircraft could be solved. For weeks, engineers inspected aircraft after they returned from their bombing runs. Engineers reinforced the damaged, shrapnel punctured hulls and then sent the planes back out. Losses mounted. Finally, an engineer looked at the planes that had returned and it hit him. They were looking at the problem backwards. He suggested reinforcing the planes in the places where they had not been hit. It worked. The planes that made it back to base didn’t need help in their obvious points of damage. After all, they returned despite that damage. The solution hinged on transcending the obvious.

Of course, I’ll admit that the obvious fix may often be the correct one. A broken window is a broken window. In the same way, the scarcity of women in corporate boardrooms says something about the treatment of women across America – in colleges and cafes, kitchens and presidential cabinets – and increasing the numbers of women leaders will do something to rectify this. But what about the less obvious benefits? The uncovered opportunities? The value of Big Data lies in its ability to provide us with  a new view of the world. It helps us to see things in entirely new ways. So too are women leaders able to contribute a perspective that has the potential to uncover exciting new opportunities for organizations.  Now think about women working WITH Big Data: it’s fresh perspective squared.