#I have arrived at a point of not measuring my content by industry standards anymore

[dkettchen]

me, procrastinating on one project with another one: it's fine they're both for content I do this for the people and the people will receive SOMETHING sooner by me working on either one 😤

[newstfionline]

Can Techie Parents Reinvent School For Everyone—Or Just Their Rich Kids?

By Ainsley Harris, Fast Company, Sept. 11, 2017

Six-year-old Tiana had just gotten her ice cream machine working for the first time, and she was triumphant. Wrapped in hot pink decorations and duct tape, the device was now capable of churning out flavors that the young scientist planned to dub “Mint Speshel” and “Tiana’s Dlitght.”

Eyes wide, Tiana turned to her teacher, Shira Leibowitz.

“Shira, this is the most important day of my career,” she declared.

Leibowitz, a founding team member of startup Portfolio School in Manhattan’s Tribeca neighborhood, recalls that story with a laugh. Portfolio School has been designed to look and operate more like the workplace of the future than the classroom of today, but no one expected students to internalize that approach quite so literally.

“They view themselves as working,” says Leibowitz, who has a doctorate in education. “They’re never learning something because one day they’ll need it, they’re learning something because they need it right now.”

As in a modern office, a typical day at Portfolio School revolves around individualized goals and collaborative, interdisciplinary projects. Tiana’s ice cream machine was the culmination of a unit called “learning is delicious,” which ran the course of the fall 2017 semester. As students explored that theme and built their machines, they learned about science (states of matter), math (measurement), and history (the commercialization of ice). When I first visited the school one morning last October, Tiana had just produced a trial batch of mint ice cream and proudly shared a bowl with me.

Portfolio School is at the vanguard of a movement of startup schools seeking to foster learning experiences, like Tiana’s, that map to the jobs of the future. Many are “micro-schools,” where students of different ages occupy a single multi-purpose space. Many are based on the Montessori method, which emphasizes curiosity and guided choice. And nearly all of these startup schools aim to personalize learning by using technology to deliver individualized lessons alongside group activities.

Perhaps it should come as no surprise that the founders of this new wave of schools are often former technology executives who have started families. In their previous roles they ushered in a new way of working, now prized across industries, which values collaboration, creativity, and iteration. They look at traditional school, with its textbooks and lock-step progressions, and see the need for revolution.

Portfolio School cofounder and CEO Babur Habib fits that profile exactly. He grew up in Pakistan, where he attended public schools, and moved to the U.S. to pursue his PhD in engineering. (He and cofounder Doug Schachtel, who manages operations, met on the squash courts at Princeton, where Habib earned a doctorate in engineering.) Early in his career, Habib designed and debugged microprocessors. Later, he cofounded an education company that was eventually acquired by Intel in 2014. After the deal closed, Habib spent a year managing the integration. Around the same time, his daughter Sophia was born.

“That was the eye opener,” he says of his stint developing educational mobile and tablet applications at the hardware processor. “I visited so many schools, talked to so many administrators.” Over time he grew to share school leaders’ frustrations. Constraints, like classroom design, limited their ability to experiment with technology.

“There’s so much room to reimagine this stuff,” says Habib. “If things are changing in the real world, why aren’t they changing in schools?”

Other parent-technologists have arrived at a similar conclusion.

In the heart of Silicon Valley, Khan Academy founder Sal Khan established a complementary lab school that he describes as “Montessori 2.0.,” infused with the type of video-based math lessons that Khan Academy has popularized since 2007. Across the country, former Google executive Sep Kamvar created Wildflower Montessori, which launched as a storefront school in Cambridge, Massachusetts, in 2014, and has since added nearly a dozen locations. And then there is AltSchool, a network of micro-schools that is the brainchild of another former Google executive, Max Ventilla. He has managed to recruit an executive team that includes parent-leaders from Airbnb, Uber, and Zynga.

“‘I want something better for my child’--that’s what’s motivating a lot of these high-tech entrepreneurs,” says Tony Wagner, a former teacher who now serves as an expert in residence at Harvard’s Innovation Lab.

Wagner, who advises Portfolio School, sees the growing interest in startup schools as both a reaction against the dominance of test-prep pedagogical regimes and an embrace of the knowledge and skills that future jobs will likely reward.

“The big leap we’re trying to make is moving away from content standards to performance standards,” Wagner explains. “Can you use knowledge, can you apply knowledge?” Demonstrating mastery of chemistry, in this line of thinking, would involve designing a study and presenting the findings, rather than memorizing the periodic table.

“Content is not as important anymore. Content is in our back pockets, literally,” Habib says, gesturing toward his iPhone. “Whatever knowledge you’ve gained, how do you apply it? That is the central thesis of this school. We feel that the creative process of taking an idea and then producing something out of it is so important, so important for the future.”

But as Habib and other parent-founders are discovering, turning lofty pedagogical aspirations into daily reality for a small group of children is no easy task.

It requires patience, for one. Habib, who previously taught physics at Stanford and authored papers on quantum dots, has had to learn how to explain the basics to tiny beginners. During one of my visits to Portfolio School, I found Habib at a whiteboard, teaching long division to an advanced 7-year-old. Habib and Schachtel are not trained educators, but they have taken a hands-on approach in their school’s classrooms and made a point of hiring expert counterparts. After recruiting Leibowitz, they signed on engineer-turned-teacher Nancy Otero, who previously created digital fabrication labs for schools in China, Brazil, and Spain.

At Portfolio, Otero installed a “Makerspace” in one corner of the rented ground floor space that the school occupies. Wire cutters, a sewing kit, and other tools hang from pegboards on the wall. There is also a soldering iron, which Portfolio’s kindergarteners wield with surprising aplomb.

“We don’t distinguish for the kids between a pencil or a scissors or a 3D printer or a laser cutter or a book or an online science simulation,” says Leibowitz. “They use what they need when they need it to learn and to create, so that it’s seamless. It’s not ‘now we’re going to technology, now we’re going to the art room.’”

Before Portfolio, Tiana was homeschooled by her mother, Jackie, who paused her Wall Street career to oversee a schedule that included piano and violin lessons and trips to the Metropolitan Museum of Art. But Jackie felt limited by her own breadth of experience: “I only knew traditional school.” At Portfolio, she says, “They have a vision even above my vision, and they can implement it.”

Though a high achiever by any standard definition--she majored in math and economics as an undergraduate, and earned a Stanford MBA--Jackie has little interest in the status markers of academic success that dominate New York’s competitive private schools.

“Giftedness--what does that mean? Winning a chess championship? It’s good for the parents to brag, but it’s meaningless for the kids,” she says. Portfolio, in contrast, emphasizes the virtues of intrinsic motivation.

“They put the challenge back to the child, and I love that,” she says. “They’re teaching how to be a self-sustaining learner. [Tiana] feels she can do anything.”

Over morning coffee and biscuits at Bubby’s Tribeca, around the corner from Portfolio, Habib and Schachtel reiterate that vision.

“It should never be more about school than learning, or succeeding just to get the right grades and get into the right school,” Schachtel says. Growing up, he logged one accolade after another--Princeton diploma, Columbia MFA--but struggled to find purpose in his studies, and later in his work. “You get on this track,��� he says.

Like Habib, Schachtel envisions that Portfolio students will one day attend top universities--but “that’s not the expectation that’s put upon kids and the driving motivator.”

Of course, if Portfolio students do happen to aim for the Ivies, many years from now, they will be ready--perhaps even at an advantage.

“Our approach of building impressive student portfolios from the age of 5 is preparing them for admissions,” says Leibowitz, who notes that top schools, including MIT, now review portfolios of student work alongside essays and other application materials.

Plus, she adds, “If [students] are still taking SATs when these guys are preparing for college, we’ll teach them strategies for the test as if it were any other project. We want all the doors to be open to them.”

For $35,000 per year--Portfolio’s current tuition rate--parents expect nothing less.

And therein lies the tension facing private startup schools like Portfolio, many of which rely on wealthy parents to get off the ground but aspire to serve children of all backgrounds by selling products and services like teaching training and project-based curricula to their public school counterparts. The steep price that Portfolio parents pay ensures that their children are taught by PhDs and given access to resources like a Makerspace. Meanwhile, at nearby Manhattan public schools, teachers with STEM backgrounds are a rare luxury, and budgets are so tight that parents routinely pay for Kleenex and other basic supplies.

If AltSchool founder Ventilla has a pedagogical bias, it is toward participatory lessons--like most of the educational entrepreneurs in this new era. “Students should be encouraged, at every stage of the learning process, to adopt an active stance toward their education,” Khan wrote in his 2012 book, The One World Schoolhouse: Education Reimagined. “They shouldn’t just take things in; they should figure things out.”

Tiana and her peers had that type of learning experience during Portfolio’s first year, and so too did Habib and his founding team. They scrambled throughout the spring to create lessons and projects that incorporated student interests, with largely promising results. As part of a unit on domesticated animals, Portfolio’s students welcomed two guinea pigs into their classroom and designed a custom house for them, complete with sensors and webcam. “They built a three-story castle,” Habib recalls with pride.

One boy, 9 years old, trained a neural network to tell the two guinea pigs apart, using the webcam video feed, so that he could analyze their behavioral patterns. An investor who happened to attend Portfolio’s end-of-year presentation described the student as “immediately employable”--to his parents’ great surprise and Habib’s great delight: “This is the first time the parents don’t know as much as the kids do.”

[jbaquerot]

The amount of data in the world has increased exponentially. It is estimated that 90% of the world’s data has been created in the last two years. Every online interaction, every smartphone input, every consumer purchase - all contribute to datasets of unprecedented size, or “Big Data.”

Each year, the Society for Industrial and Organizational Psychology (SIOP) holds a conference where the latest trends and advancements in employment assessments are presented and discussed. In the panel: “Making Better Business Decisions: Risk and Reward in Big Data,” industrial-organizational (I-O) psychologists on the bleeding edge of assessment science discussed the implications of these massive new datasets for predicting job performance.

These were the panelists:

Richard Guzzo, Consultant at Mercer

Nathan Mondragon, Chief I-O Psychologist at HireVue

Charles MacLane, retired, formerly at OPM

Richard Tonowski, representing the U.S. Equal Employment Opportunity Commission

For Industrial-Organizational (I-O) Psychology and pre-employment assessments, Big Data holds huge promise.

Big Potential from Big Data

Consider the classic pre-hire assessment: 200+ questions that to the test taker seem repetitive and redundant. But to the assessment creator, 200+ responses are the bare minimum required to make an accurate prediction about future performance. Each new data point decreases the likelihood of a mistake and increases the predictive power of the assessment.

According to Richard Guzzo, the panelist from Mercer, these are the “classical shackles” of solving problems and making changes. The predictive modelling approach is there - it has been for decades. But if there is not enough information to be considered by the model, it cannot be predictive.

In other words, larger datasets mean more predictive accuracy. You can see how the millions of data points gathered from new data sources and collection methods could be immensely rewarding when performing predictive analysis.

Finance, marketing, and other industries are already leveraging these huge datasets to make more predictive decisions. So with data collection at historically unprecedented levels, is it time to change the pre-employment assessment game?

Gathering the Data

As mentioned above, traditional data collection relied on the written responses of job candidates. Now, with the increased ability to make sense of “big” data, there are three primary data collection methods being used by assessment innovators:

Social & Web Scraping. Assessment data is gathered from candidates�� social media pages, online profiles (LinkedIn, Facebook, etc) and other online interactions.

Game data (Gamification). Assessment data is gathered from candidates’ input in games designed to elicit responses that indicate cognitive and other job-specific skills.

Video Interviewing. Assessment data is gathered from video interviews: non-verbal communication, word choice, intonation, etc. Altogether, each interview provides over 25,000 data points.

Each of these methods makes use of new data collection techniques, like machine learning, to make sense of what would be otherwise jumbled and nonsensical data.

Analyzing the Data

While the content of the data and the way it is extracted have changed, the I-O approach has not. The type of statistical test used to crunch the data and create a predictive output is the same as is used when creating assessment questionnaires.

Nathan Mondragon clarified: “There are definitely different names, and different methods of extracting the data, but not different statistics.”

A Bit of Background: Validity and R-Values

Validity, put simply, is the applicability of an assessment to actual job performance. There are three main types of validity:

Criterion-related validity: indicates the correlation between test performance and job performance. A test with high criterion-related validity will accurately identify top performers.

Content-related validity: indicates the relevance of test data to actions performed on the job. A test with high content-related validity will accurately measure specific job skills.

Construct-related validity: indicates the test measures the characteristic it claims to measure. A test with high construct-related validity will accurately identify and measure certain abstract traits, like aptitudes.

There is often overlap between these three types of validity in a single assessment, but it is not guaranteed.

R-values represent how valid the test is. Here’s a table showing what you can expect from tests at each given r-value.

For example, an assessment with a criterion-related validity of .35 is going to consistently identify top performers. R-values above .4 are largely unheard of.

So What’s the Value Added by Big Data? And Can It Be Used in All Situations?

According to Richard Guzzo, the value of Big Data lies in the fact that it can be used in all situations.“We’re not just dealing with a candidate’s responses to a written questionnaire anymore,” Guzzo explained. “The huge variety of data lets us apply I-O techniques to a range of issues at a rapid pace.”

Nathan Mondragon examined similar implications: “Big Data can make the assessment cheaper, better, and a better candidate experience,” he said. Reflecting on the 25,000 data points collected from a video interview, Mondragon asserted that we can do away with tests - lowering costs and concerns while making a better experience for the candidate.

“With our validated, I-O designed video interview questions, we get a .3 - .4 (r-value),” he explained. “When the video interview is designed correctly and considered correctly with I-O techniques, there are good reasons to reconsider long-standing methods.”

“There are definitely improvements to using biggish data,” Charles MacLane elaborated. “We might have been missing a critical piece of data in consideration or excluding a group based on a piece of data not being considered because of volume limitations.”

The ability to crunch larger datasets can make pre-employment assessments more agile, predictive, and convenient.

But what about the legal side of things? Richard Tonowski, the panelist representing the US Equal Employment Opportunity Commission, reflected on the potential for adverse impact when considering new sources of data.

The EEOC Perspective

According to Richard Tonowski, the EEOC does yet not have an official position on Big Data.

“At an open commission meeting last year, my perception was that the commission was open but suspicious,” Tonowski explained. “We have to get straight what we are talking about - what does a Big Data-driven assessment provide an alternative to?”

Potential Legal Issues

He questioned why an employer would perform a huge study using new methods if they could get an r-value of .3 with a traditional "off-the-shelf" assessment. As companies continue to gather more and more data, he sees problems arise when they attempt to put together “Big Data” assessments themselves:

“In order to defend a selection system you need to have good construct, content, and criterion - in many new cases this might be missing,” Tonowski elaborated. “If you create an adverse impact when using a different methodology, it doesn’t matter what interesting interaction effects you have. The courts will jump on that.”

Nathan Mondragon agreed. “Like with any traditional assessment, if you have bad predictors or criteria, you still have bad validity. You still need to get good, job-related data to get good validity.”

For example, video interview data with standard interview questions gets pretty good results (around a .2). But if specific questions are created based on KSAs and KPIs, validity shoots up to a .4 (r-value) - without adverse impact.

“If we draw upon standard interview questions, we get decent results from video interview data. But when we look at KSA’s and KPIs and create questions based on good job analysis and good job design, we’ll go from a .2 to a .4 based on the better questions.” - Nathan Mondragon, HireVue

"What Nathan just said (regarding the use of good, job-related data) we would consider a best practice," Tonowski responded.

Essentially, if a new assessment is not fully vetted and discriminates against certain demographics, it doesn’t matter how well it predicts job performance. But with the proper application of I-O techniques, this shouldn't be an issue.

What about social & web scraping?

In regard to social scraping, Tonowski’s advice was to “tread lightly.” In some situations, like screening for suitability or cultural fit, it might not be an EEO issue. And if you’re using it to assess qualifications, you might be on shaky legal ground.

But if it is used as a mass screen out up front, like in the instance of criminal conviction history, it’s an issue because it causes adverse impact.

Big Data: Risk, Reward, or Both?

The predictive power of massive datasets holds huge promise for a field that traditionally required lengthy, time-consuming questionnaires to make predictions.

But unlike the finance and marketing industry, talent acquisition has a duty to ensure that the results of their analysis have no adverse impact on protected groups. Understanding why and how an algorithm arrives at its conclusions is necessary to making changes that eliminate adverse impact.

So might there be risk to using “Big Data” when making screening decisions? Perhaps. But the consensus seems to be that, with the proper application of I-O techniques, Big Data can be leveraged for greater predictive power with none of the risk.