4 Types of Data Analytics for Educators

Data and Education?

A quick survey of the Web turns up many helpful pages on data analytics. But because data is the currency of our now digital economy, the articles typically focus on the business world.  Any mentions of data in terms of education usually surface one of three understandable perspectives: protecting student privacy, boosting engagement (i.e., $) and the mostly misguided use of high-stakes, though meagre, metrics to represent human learning.

Furthermore, some educators can be hesitant about “data.” Maybe data seems like numbers and graphs while our role is to nurture growth in human beings?  But the right data is an asset in our very human task as it can provide some evidence about how things are going. Quality data, easily accessible and effectively visualised is one way to gain insights on such questions as:

  • Are students learning?
  • What are they learning?
  • What aren’t they learning that could help them grow?
  • What pedagogical approaches are the most effective for which students?
  • How can our school and system use these insights to keep getting better?

This post will provide background on the four types of data analytics and offer four tips for educators. Let’s start with a graphic that we can unpack:

4 Types of Data Analytics

What are the Four Types of Data Analytics?

A handy way to understand data analytics is to view it in terms of what it can do for us:

  • Describe – what’s already happened
  • Diagnose – why did it happen
  • Predict – forecasts what might happen in the future
  • Prescribe – recommends what to do

The first handy thing to notice is that the “D”s are about the past and the “P”s are in the future. The other useful dimension is the common sense relationship between value and complexity: the simplest data is also the least useful, whereas the most complex has the potential to add the most value.

Now let’s see how the the four types of analytics align with data sources schools already know. I’ll also highlight how complexity and value factor in.

Descriptive Analytics

Descriptive analytics can tell us “what happened.” This is by far the most common type of data used in schools.  When teachers give tests at the end of a unit and assign a mark or compile semester reports, they are generating descriptive data. Other kinds of descriptive data schools might collect are attendance records, participation in co-curricular activities, literacy progressions, feedback surveys, etc.

Value and Complexity

Teachers and schools invest extraordinary resources collecting such information, so it’s unfortunate that these only provide a simple “backward glance”.  As much detail as a teacher might consider before assigning a result, in the end, it’s typically an A-E grade or 0-100 percentage.

Applying Analytics

Probably the main applications of these datasets are ranking students and potentially serving as extrinsic motivation.  However, careful analysis of such descriptive data across a school can help identify variability (and success?) among teachers and faculties, identify patterns for specific student cohorts, and compare academic achievement in the context of things like school engagement and well-being.

Diagnostic Analytics

Diagnostic analytics offer more in-depth insights into student performance or ability. In Australia, when people think of diagnostic data in education, what comes to mind are standardised or “high stakes” tests such as the National Assessment Program – Literacy and Numeracy (NAPLAN) or ACER’s Progressive Achievement Tests (PAT).  In the primary years, many schools use commercial benchmarking programs (such as PM, F & P and Probe). Some online programs / apps also provide diagnostic data. In the classroom, many teachers use formative assessments and rubrics to assess students’ skills. What these all have in common is that, just like a medical doctor reviewing blood tests or x-rays, diagnostic assessments provide greater detail about what’s going on.

Value and Complexity

This greater detail we can call “granularity”. So the real benefit of, say, a NAPLAN Reading assessment is not to find out what Band a student is in, but to gain insight into a student’s strengths and gaps in core skills related to the domain such as “connecting information,” “applying comprehension” or “inferencing.” With this greater degree of complexity, you can see how the value increases.  Rather than try to help that student “read better,” we can target teaching to fill gaps and extend strengths.

Applying Analytics

Taking advantage of the diagnostic assessments used in a school is a challenge, mostly because the data is buried and not easily visualised. When teachers, faculties, grade-levels teams and leaders can quickly see such gaps and strengths across core skills at the student, class and cohort levels, each stakeholder is empowered to support targeted solutions.

Predictive Analytics

Predictive analytics can offer insights into “what is likely to happen.” It uses the results of descriptive and diagnostic analytics to predict future trends. As such, predictive analytics forecast likely outcomes to be pursued or avoided.  Many schools employ data consultants to conduct statistical reviews of past performance in high-stakes measures, especially Year 12 results such as HSC, VCE and Advanced Placement tests.

Value and Complexity

Clearly, because solid predictive analytics is based on equally solid descriptive and diagnostic data, it is more complex to gather and analyse.  The reason schools often outsource this analysis to data experts is because of the statistical analysis required and also the savvy to integrate appropriate data sets. Equally complex is the interpretation of the analysis so that patterns aren’t seen as causative when they might really only correlate (e.g., high participation in co-curricular activities may correlate with high achieving students, not cause the high achievement).

Applying Analytics

Besides employing data scientists, schools can begin use of predictive analytics by identifying what information they most value about students and their learning. This might require introducing new descriptive and diagnostic measures and then taking several years to grow these data sets.

Prescriptive Analytics

The fourth type of analytics results in actual prescriptions of “what action(s) to take” to solve a problem in the future or to take full advantage of promising trends seen in the predictive analysis. Many schools engage in pedagogical initiatives such as Reading Recovery, Maker Spaces, STEM, writing across the curriculum or wellbeing programs, but such decisions typically lack a robust foundation in hard data. It’s not that such initiatives are bad, but when they are begun without targeting an evidence-based issue with aligned pedagogical solutions, measuring success is ad hoc at best. One downside of such initiatives lacking credible success criteria is the drain on staff morale and change fatigue. We are all motivated when we perceive ourselves as being effective.  Without measures, this is difficult.

Value and Complexity

Like predictive analytics, prescriptive is also based on data from each of the other types and thus requires both quality data and insights (see the next article on Tips for Educators).  Obviously, the value-add of such predictive analytics is great, but the complexity needed to combine and model the data is comparably great.

Applying Analytics

Both the value and complexity of prescriptive analytics suggests not only the participation of experts, but also technology. In fact, this is where many tech companies get involved and apply Artificial Intelligence to the rapid modelling of data.  “Big data” sets are needed for algorithms to apply both unstructured and structured analysis to tease out reliably demonstrated outcomes. A good example is the increasing sophistication and accuracy of tech giants like Google and Amazon to make suggestions based upon constructed profiles for each user.  In this way AI can move beyond more simple “if/then” predictions to an assurance that specific actions will lead to the desired outcome. Extensive work is being done in this area at the university level.

Where to Next?

As you can see, most schools would collect, use and benefit from each of the four types of data analytics.  Yet, it’s the rare school that has a mature approach to managing and employing the data circulating through its software, systems and folders. Now that you have more background on the types of data and analytics, please read the next article offering 4 Tips on Data Analytics for Educators.

A Big Change for Tom

What a glorious new beginning!

Back in 2014 I wrote a similar post at a time of transition. Today opens a new chapter in the unfolding story of how a high school English teacher from California morphs into a Web-based educator and contributor to the next era of education.  To re-cap, earlier parts of the journey included a fellowship at San Diego State University where we developed the WebQuest model, then a move to Australia and time as a Web developer and Ed Tech consultant with plenty of writing, software design and keynoting…  until I “got my first real job” since teaching when I joined Hobsons in 2014.  Although I explored positions in school leadership and returning to consulting, it was clear that the exact job didn’t matter so long as I was:

  • using all my skills
  • working on a great team
  • making a difference in education

Things clicked when I met the leadership team at Hobsons Edumate:

From Edumate …

For the past 2 + years I’ve really loved working with the great team at Hobsons’ Edumate.  As much as I’ve enjoyed this shift from the sometimes lonely life of an independent consultant, that fact that the Edumate suite also includes modules for attendance, enrolment, finance, and calendaring means that my passion for improving teaching and learning must be balanced with the overall needs of Edumate’s clients. I got and fully supported this. Those times I was able to harness the development team to work on the curriculum aspects of the software, I felt as though I was contributing – yet while other development needs rightly took precedence, I sometimes felt I wasn’t having the impact I hoped for. Recently the name “Literatu” began popping up with both current and prospective schools, so we decided to meet up…

To Literatu!

What I saw so impressed me that my curiosity was piqued and before long we’d kicked around ideas and found that my obsession with richer teaching and learning matched nicely with the powerful analytical insights provided within a very slick and user-friendly platform.  However, more than the software, I was very impressed with the Literatu leadership – Mark Stanley and Lidija Loridon. They definitely understand assessment, analytics, user interface and what schools need to turn data into insights.  Because this is only the first day on the job, of course there is a lot I don’t know (yet look forward to learning — which is a big part of the excitement!).  In the coming months (and years) I will share more about the power of this technology to humanise teaching and learning as I dig into it and we evolve it.  In particular I am (delightfully) tasked with helping schools and their teachers get early wins analysing their data and then build a plan where they nurture a culture of continuous improvement informed by their own unique goals and processes coupled with powerful data analytics. Look for more posts, Webinars and professional learning and consulting to support this journey into the future.  

Spector Spyware as Big Mother

As I consult with several groups, one common thread is a serious consideration of how to support 1:1 mobile learning while confidently keeping the students who are bent on pushing the boundaries from getting into too much mischief.  As the Big Mother theme highlights, schools and systems identify their philosophy of education with the stand they take on the “Clamp Down” to “Free Reign” continuum.

Essentially I am a pragmatic idealist.  I believe the foundation of a school must be based on trust and belief that given the opportunity, people – students and staff – want to do the right thing.  That’s the idealist part.  On the pragmatic side, we come from all backgrounds, personality-types and motivation levels, so the foundation of trust includes the corollary that breaking the community’s trust carries grave consequences.  With freedom comes responsibility.

Add to these points the belief that The New WWW (Whatever, Whenever, Wherever) provides a greater temptation for today’s students than previous generations ever faced.  When a mobile device enables immediate gratification or stimulation 24/7, making helpful choices needs to be a metacognitive task.  So one option I’m encouraging clients to consider is a compact with students to install Spector Spyware (or such) onto their tablets and laptops.  Not, as the Big Brother it’s designed to be, but as what you could call a “Jiminy Clicket” (or not).  Students will know that their online actions can be replayed and will be – not to catch them out, but for them to review and reflect upon.

The point isn’t to see if students ever Stumble Upon (or search) naughty bits, but to help raise their awareness that, for example, 2/3 of their time spent researching is clicking links and 1/3 skimming text.  Or that the combined time spent peeking in on YouTube is greater than the time spent writing an essay.  Maybe that real-time interruptions like MSN, chat or friends’ updates from social networks, combined with all the above, leave nothing longer than 3 minutes for focused concentration.

Thus the key is not to use spyware as a threat, but as a non-judgmental witness who records what we get up to.  When it’s all too easy  to amuse or intrigue ourselves, a little help from friends might be a useful strategy.

How are others addressing this challenge?  I’d love to hear about places taking the highroad, not battening down the network, and how they go about it.  This is especially tricky on a systemic level, beyond the culture of the classroom where students are “left to their own devices.”

“Big Mother” as Cognitive Tutor

tutorIn my last post, I suggested that education would do well to mine the wealth of information that can be derived from digitally tracking student movements. A lot can be learned through amassed patterns of student use within software virtual environments and actual physical environs. Today Education Week reports about a New Breed of Digital Tutors Yielding Learning Gains. The article focuses on a school district in Everett, Washington where:

all of Everett’s high school students have a choice in signing up for Algebra 1, Algebra 2, and geometry: a traditional class or one that mixes teacher-led lessons with a sizable dose of machine-based tutoring.

Later in the article, the point is made:

Studies suggest that, on average, students who use Cognitive Tutor make learning gains that roughly translate into the equivalent of as much as one letter grade—the difference, in other words, between an A and a B.

So here’s one more example of how technology supports the individualization of skill-building in the cognitive domain. With teacher-shortages in many areas and a graying of the force, it’s not difficult to see how this trend will continue and become more sophisticated.

Big Brother or “Big Mother?”

A few years ago Coca-Cola ran a promotion called “The Unexpected Summer.” In it a combo cellphone GPS device was rigged to look like a can of Coke and placed in over a hundred 12-packs around the country. A companion Web site allowed people to watch the blips as satellites tracked the lucky winners within 50 feet of anywhere the US.

Recently a few news items reminded me of this and the role of technology in keeping track of our whereabouts. Hitachi has developed a Radio Frequency Identification (RFID) “powder.” The chip measures .05 millimeters square and 5 microns thick, about the size of a grain of sand. Another interesting development in the world of RFID was a patent taken out in February by Kodak for an edible RFID chip. Among other potential uses is for nurses to know if you’ve taken your medicine.

Less invasive might be the GPS sneakers now on sale from Isaac Daniel. The sneakers work when the wearer presses a button on the shoe to activate the GPS. In some emergencies — such as lost child or Alzheimer’s patient — a parent, spouse or guardian can call the monitoring service, and operators can activate the GPS remotely.We could add to this list the cell phone services and GPS car units designed to let parents know where their children are – out of harms way, one hopes. What will be very interesting as these technological developments continue is who monitors them and for what purpose.

In 1984, Orwell invoked a Fascist “Big Brother,” representing the power and interests of the state. In “Big Brother – the TV series,” a house and voyeuristic citizens take the role of omniscient observer of our every move. As Web 2.0 technologies converge with mobile communications, multi-nationals and corporate marketers anticipate the day when our physical location and long tail of previous purchases unite in an endless stream of opportunities to “impulse buy.”

Stopping this movement isn’t within our means. What might be – for those of us who are parents and teachers – is to advocate and champion a human side to this potential. In other words, demand educational applications that side-step Big Brother in favor of “Big Mother.”

  • We know what people surf for, but do we have an algorithm to help us match students’ learning to their interests?
  • Databases keep track of what we buy online, but can teachers access a similar tool that provides information about an individual’s knowledge, skills and attitudes?
  • Social networking sites match us up with thousands of “friends,” but can the software also help us reflect on the wisdom of our choices?