Every state that was a winner in the Race to the Top (RT3) has millions of dollars to develop a P-12 Longitudinal Data System (LDS). And sure enough, our friends over at Achieve, developed RT3 primers that states could use to write their proposals. The Gates Foundation also provided help to states to write their proposals which were then submitted to the U.S. Department of Education (ED).

Georgia was one of the winners of the RT3, and since I have lived and worked in Georgia for about 40 years, I have made a point of examining and writing about Georgia’s RT3.

In a recent post, I described how the funds that Georgia received ($399 million) are distributed among the four main goals of the project.  The Georgia Department of Education has $39,000,000 to develop a P-12 Longitudinal Data System, one of the main areas of work. The purpose of LDS is to “build data systems that measure student growth and success, and tell teachers and principals about how they can improve instruction” (Georgia Department of Education, RT3).

If you dig deeper, you will find that, according to ED and Achieve, educators need the “right data” and it must be put in the “right hands.”  In the Georgia RT3 plan, as well as in other states that received funding, “the idea is to develop a warehouse to host data across the P-20 spectrum. This data warehouse, known as the State Longitudinal Data System (SLDS), will support statistical and business intelligence tools as well as reporting (Georgia Department of Education, RT3).

Further investigation indicates that the greatest purpose or goal of the SLDS in each state is to improve educator’s decision-making to “improve student achievement.”

According to the latest performance report, Georgia RT3 has hired a vendor to develop the P-20 system.   All of this is organized through the Governor’s Office of Student Achievement (GOSA).  In order for teachers to really use this system, technology training is the first order of business (after the Warehouse is built).  Early reports show that some of the 26 school districts cooperating in the Georgia RT3 are not accessing the present data system.  Apparently the State will give more resources.

But, please keep in mind, if the goal is to “personalize education for each student” then every teacher needs to become an expert in “data mining,” and instructional innovation.

Is Data Mining the New Oil?

Credit to Marvin Bredel, Flickr

On Hacked Education, Audrey Watters writes about the emerging fields of education data and learning analytics in her post, Student Data is the New Oil: MOOCs, Metaphor, and Money.  The post is a speech she delivered at Columbia University, on October 16, 2013.

At the beginning of her speech she questions the promise of large data systems and learning analytics.  She says:

The promise of learning analytics, so we’re told by education researchers and — with much more certainty and marketing finesse — by education companies, is that all this data that students create, that software can track, and that engineers and educators and administrators can analyze will bring about a more “personalized,” a more responsive, a more efficient school system. Better outcomes (which we can translate cynically as: higher test scores, higher class and college completion rates).

The claims about big data and education are incredibly bold, and as of yet, mostly unproven. Watters, A. (2013, October 17) Student Data is the New Oil. [Web log post]. Retrieved October 22, 2013, from http://hackeducation.com).

In my view, we have to wonder how a high school chemistry teacher with 150 students can use data analytics to personalize chemistry for each of her students.  What about the mathematics teacher, who not only teaches algebra, but also teaches geometry and advanced math?  How does he personalize mathematics for every one of his students?  And how about the art teacher?  Is personalized learning in art the result of using analytic data to direct the students?  And the special education teacher.  How does she use results to help her students?

If technology is used to mine the data, is the solution to personalized learning, technology?

Petrology or Pedagogy?

Will the mining of the mass of student data lead to pedagogies that will help students understand chemistry, mathematics, or art?  Will the mining of data simply be an exercise in petrology, or will it uncover new pedagogies?

Audrey Watters cites the online company, Knewton, which claims to provide personalized solutions using their technology to move students through course material “at their own pace.”  She points out how Knewton uses PR spin to claim that they offer “adaptive learning” by means of its technology.  She says:

Take Knewton, for example, one of the corporate leaders in the sector. Once in the highly lucrative business of test prep, Knewton has rebranded to offer “adaptive learning” — partnering with publishers to create content delivery and assessment software that moves students through educational materials “at their own pace.”

That’s the PR spin, at least: with big data, Knewton engineers can now precisely identify a student’s strengths and weaknesses and “learning styles” (ignoring, I should add here, the evidence that learning styles do not exist) and guide students through the next-best content nugget so as to learn with maximum efficiency. As a recent story about the company put it, “If you learn concept No. 513 best in the morning between 8:20 and 9:35 with 80 percent text and 20 percent rich media and no more than 32 minutes at a time, well, then the odds are you’re going to learn every one of 12 highly correlated concepts best that same way.”

Knewton boasts that over a million data points are created by students on its platform every day (emphasis mine), all of which feed its algorithms and its recommendation engine, all in turn in the service of delivering lessons that it claims are perfectly and personally adapted to each and every student (Watters, A. (2013, October 17) Student Data is the New Oil. [Web log post]. Retrieved October 22, 2013, from http://hackeducation.com).

As I noted earlier in this post, the Georgia RT3 intends to develop a data warehouse that can be mined by researchers, educators, administrators, and policy makers who will be provided resources and professional development to encourage better data-driven decision-making.  I’ve reached out to the GOSA asking for permission to use the data warehouse, but so far I haven’t heard from them.

What Student Data?

One of my reasons for wanting to use the P-20 Longitudinal Data System is find out what student data they are using and storing in the data warehouse.  No doubt they will include transcript type data such as grades, attendance, behavior, age, etc.  And no doubt we will find student test scores accumulated over the years in every subject that was tested.  But what kind of data, beyond these, will enable educators to “make better data-driven decision-making,” especially to personalize student learning?  Will this data be valuable to the day-to-day teaching of our chemistry, math, or art teacher?

If you read Audrey Watters speech, you will be astounded by the amount and kind of data that could be collected on students that data-driven decision makers will mine.  I read through her speech and assembled the following list of student data (Table 1).

Table 1. Types of Data Created by Students for Data Mining (Watters, A. (2013, October 17) Student Data is the New Oil. [Web log post]. Retrieved October 22, 2013, from http://hackeducation.com).

How will this kind of data be helpful to a teacher?  How will this data be helpful to education companies such as Pearson?

The phrase data mining should not be taken lightly.  It is built into the $4 billion Race to the Top, and those states that received funding must create data warehouses that can be mined by many constituent groups.

What if We Had More Data?

Watters argues that we should imagine what if we had more data?  How would the data be used?  What predictions about student behavior or performance could be made?  She says (from – Watters, A. (2013, October 17) Student Data is the New Oil. [Web log post]. Retrieved October 22, 2013, from http://hackeducation.com):

• We could identify which students are likely to be the most successful academically. (In a future where graduation rates are tied to financial aid availability, imagine how this might shape schools’ enrollment policies.)
• We could identify which students are likely to be dropouts. (Of course, then we have to ask: what do we do then? When do we intervene? How do we intervene? To what end?)
• We could investigate how students’ performance in Algebra I is tied to their credit score later in life. We could investigate how their performance in The History of Western Civilization is tied to their voting patterns.
• We could identify the lessons and the lectures and the assessments sets that “don’t work.” (You hear this from the folks at Coursera a lot who argue that MOOCs allow them to “fail fast” in this respect. Or, as my friend Mike Caulfield argues in response, you could actually hire good instructional designers and produce quality work from the start.)
• We could identify which students are likely to make great biochemistry majors. (You have to wonder here, if the folks who write these algorithms would even suggest people become art history or creative writing majors.)
• We could identify which students are likely to be successful entrepreneurs. We could identify which students are likely to become wealthy alumni and reliable donors back to the school.
• And to bring things full circle, we could identify which students are likely to become radicals and dissidents. We could share that data with administrators and/or with authorities.

Whether this is an exercise in data mining, petrology, or uncovering new pedagogies, we wonder how the millions of dollars being spent by the RT3 in Georgia will benefit students in its schools?

What do you think about data mining?  In what way will this data be effective in personalizing learning?