Educometrika
PT|EN

Data science applied to education

Data integration, statistical and predictive models and monitoring systems to support decisions in school systems.

Education data analysisArtificial intelligence in educationEducation indicatorsPsychometrics and IRT

01What problem this area addresses

Departments accumulate data from many systems but rarely manage to link it on a regular basis. Questions such as "which students are at risk of leaving school?" require combining attendance, performance and school history.

Data science applied to education structures this data and builds models and tools the team can maintain and use.

02Questions education departments often face

  1. Q1How can enrollment, attendance and assessment data be linked reliably?
  2. Q2Can at-risk students be identified early enough to act?
  3. Q3How can a data routine be set up that does not depend on a single person?
  4. Q4Are the models transparent and fair to all student groups?

03How Educometrika approaches the problem

  1. 01

    Data architecture

    Mapping sources, linkage keys and update flows.

  2. 02

    Modeling

    Models chosen for the question, with performance and bias assessment.

  3. 03

    Tools

    Dashboards and alerts built into teams' routines.

  4. 04

    Sustainability

    Documentation and training so the department can maintain the work.

04Data and methods

Data

  • Enrollment, attendance and transfers
  • Assessment results
  • School history
  • Socioeconomic and territorial data

Methods

  • Data integration and cleaning
  • Statistical and multilevel models
  • Predictive risk models
  • Spatial analysis
  • Automated data routines

05Possible deliverables

Integrated, documented databaseRisk alert modelMonitoring dashboardsAutomated update routinesTechnical documentation and training

06When this kind of support makes sense

  • When the department wants to track students more often than once a year.
  • When introducing early warning systems for absenteeism and dropout.
  • When analyses depend on repeated manual work.
  • When there is interest in using predictive models responsibly.

07Related topics

08

Is your system facing a similar problem?

Describe the context and the open questions. The team will assess how it can contribute.

Talk to the team

All areas

Consulting for education departmentsTechnical support for state and municipal education departments across the whole policy cycle: from diagnosing the system to evaluating results.Education public policyDesign and implementation of education policies and programs that account for the real conditions of execution in schools.Education data analysisOrganizing and analyzing enrollment, attendance, progression and learning data to answer concrete management questions.Educational assessmentPlanning and developing learning assessments that produce useful information for teachers, schools and management.Psychometrics and IRTMeasurement methods that ensure an assessment measures what it intends to and that results can be compared.Education indicatorsBuilding indicators that show the system's situation clearly and allow targets to be tracked over time.Policy and program evaluationEvaluations that check whether a policy was implemented as planned and whether it produced the expected results.Artificial intelligence in educationUsing artificial intelligence on concrete school system problems, with risk assessment and without adding complexity for schools.