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Dreambox Learning: transforming education with big data

DreamBox Learning is an intelligent, adaptive learning platform that accumulates 50,000 behavioral data points per hour per student

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"You may have heard the term “big data” in reference to companies like Netflix, Google or Facebook. It’s the collection of all those little data points about your choices and decision making process that allows companies to know exactly what movie you’re in the mood for when you plop down on your couch with a bowl of popcorn after a long day. Recently, big data has also made a foray into the educational realm. Whether through information gathered through standardized testing or the use of adaptive learning systems, big data is well on its way to completely transforming K-12 education." –Dan Kerns

Company overview

DreamBox Learning is an intelligent, adaptive learning platform.  The company’s has nearly 1,800 digital math lessons for students in grades K-5.  By gathering responses to questions, the software continually assesses and reassesses a student’s knowledge base to guide him/her through the material via a customized path and pace using sophisticated algorithms. 

Data analytics personalize learning experience

One of the classic struggles for teachers in the traditional classroom is to differentiate their instruction to meet the varying levels and unique needs of their students.  Lacking appropriate resources, teachers often meet this challenge by teaching to the average student.  With Dreambox, teachers can effectively personalize learning for each student, providing effective remediation for those who have fallen behind as well as engagement in challenging material for those who have outpaced the majority of the class.

Not only does Dreambox track students’ answers to problems, it also logs how quickly the problem was answered, how many hints were used, etc.  And because the software uses interactive virtual manipulatives, it can identify the strategy employed to solve the problem or the specific deficiency that led to an incorrect answer.  This allows DreamBox to formulate a rich profile of each student, including their optimal pace of learning and preferred learning styles. 

On each student, Dreambox’s software accumulates an average of and 50,000 behavioral data points per hour.  In turn, the system uses this fine-grained data to adjust subsequent content, optimizing the learning experience based on the individual needs of students. 

Data drives instruction and administration

This data also serves as a source of feedback for teachers to inform their future instruction.  Rather than waiting for formal assessments (classroom tests or state exams), a teacher can get real time information on how the class is progressing.  Teachers often put students to work on Dreambox for a half hour block a few times per week.  Additionally, with this frequent feedback, teachers are able to perform more timely and targeted interventions, one-on-one or with a small group of students who are struggling. 

Dreambox also uses its data to deliver insights on student proficiency at the school and district level to school leaders and district administrators, who can use it for evaluating teachers, informing professional development, identifying best practices, and communicating progress to parents. 

Big data helps understand the needs of unique students

Behind the scenes, Dreambox is looking at its data across millions of students and fine tuning its algorithms.  This large swath of data is particularly useful in understanding the learning of students who are multiple standard deviations away from the classroom mean.  Most teachers will rarely encounter these students over the course of their careers and therefore lack the experience needed to develop effective teaching techniques.

Ancillary services ensure quality implementation

Though lower margin, Dreambox has built out professional development training services to sell alongside its SaaS product.  The company has a vested interest in ensuring quality implementation of its software.  If it just pumps out the product without providing necessary support, this could result in suboptimal results and reputational damage.  Thus, ensuring teachers and school leaders are fully educated on how to run reports and interpret the student data is critical. 

Value capture not currently tied to student outcomes

Dreambox prices its software on a license basis with the cost to schools at $20 per year per student and the cost to parents at $12.95 per month per child.  Though attempting to monetize the parent-pay market, Dreambox’s long term success is inevitably tied to wide scale adoption in schools.  

Given how inundated school and district leaders are with promising new technologies, Dreambox might consider employing an outcomes-based pricing strategy.  This would de-risk adoption by school districts and match Dreambox’s value capture to improved student outcomes.  The additional scale achieved via this creative pricing structure would provide additional student data points to fuel improved algorithms and the value proposition of Dreambox.    

Sources: Edsurge, Dreambox


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I agree that the potential for data to improve outcomes in education and enable personalized learning is really exciting. A few thoughts that crossed my mind as I read your post:
1) I wonder if a SaaS pricing model changes the way districts have to do their purchasing. In the past, I would presume budgeting was done on a yearly basis at the school district level, and decisions were made about which textbooks to purchase for the next year. I would also think that for now, products like Dreambox are additive to existing spend on things like textbooks and other teaching materials--personalized learning hasn't yet supplanted more traditional modes of instruction. I wonder how much further school budgets can be stretched to accommodate new technological products and the level of hardware required (eg student tablets) alongside the programs.
2) I wonder what the traditional publishing companies (eg Pearson, Houghton Mifflin) are doing in the digital/ big data space. It seems like they have a lot to lose if smaller players like Dreambox have more technologically sophisticated offerings that make blended learning environments possible.
3) I worry that schools don't have a sophisticated data strategy to prevent ed tech vendors from adopting a walled garden approach where they make interoperability very difficult. The full potential of data in education is realized when the complete set of data on a given student-- whether that is assessment data, attendance/ grades, performance across multiple subjects and over time--can be integrated together to inform teaching approaches.

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