Data Science Transparency

Data Science / Transparency

Comparative K12 Analytics

Data Science

The future NLET envisions is one in which every person who wants one has a universal transcript that securely transcribes formal and informal learning, work history and social identity.

NLET bases this work on what we call Learner Identity which we are seeking funds to study, determine open standards and build pilots to be tested by high school and college students and adult learners

Approach: Bringing data science to learning 

For years, school data has “flown away” from the students, educators and education leaders who generate it.

Student data is generally part of compliance exercises when school districts turn discrete data sets over to the State or Federal Government to justify progress or budgets. In school organizations, data seldom functions as a deeper learning asset.

While there are robust analytics products, they generally do not support or are “owned” by students and the families, nor do they analyze in real-time as part of dynamic recommendation systems or triggering systems. Outside of education, leaders expect that data can provide meaningful insights into the work of their organizations.

Data scientists are developing new ways to provide analytic insight quickly and seamlessly, and to use data visualization techniques to explain what they find.

Focus: “K12 Data Lab”

NLET is actively researching and developing new data system approaches for K12 data in California. 

NLET is developing tools and services related to the use and understanding of K12 Data in California. The effort has included pilot activity with California County Offices of Education and school districts. The effort is to move toward true data science systems that can model, predict and recommend curricular, instructional and training needs to affect outcomes where performance is deficient.

Ultimately, students across schools and districts should have data points built on frequent quizzes, recording interacting with content, and producing evidence that can be collected and measured in real-time as formative data available to students, teachers and administrators.

Current Activity:  Preparing California districts 

California has moved to a new state funding model for school finance that requires districts to produce measures of their success in a number of areas to justify the financing required for local or regional needs. In many cases, the data supporting the planning and reporting process does not use data well or in a meaningful way. NLET and its partners in California are working on systems to assist California districts provide evidence and measures to justify their reports, going beyond simple compliance exercises.

Current Objectives: Partnering for change 

NLET is preparing proposals to move from simple analytics, or reporting of tabular and comparative data, to systems that can collect and produce statistic insights that can be used to predict outcomes and measure against results. These systems are not uncommon across the spectrum of e-commerce, consumer applications and mobile apps, but are not utilized to build models in K12 for use at the student level. NLET is preparing proposals to fund activity with leading research institutes familiar with big data, data science and sensitive human subject work in order to experiment with entirely new models.