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FLN — Foundational Literacy & Numeracy Assessment Platform

A large-scale, personalized assessment system that helps teachers measure, track, and improve every student's Foundational Literacy and Numeracy (FLN) outcomes — from automatic question paper generation to scanning answer sheets and instant, profile-driven evaluation.

Table of Contents

What is FLN?

Foundational Literacy and Numeracy (FLN) refers to the basic ability to read with comprehension and perform simple arithmetic operations — the core skills every child needs before they can meaningfully engage with the rest of their school curriculum. It typically covers children from pre-school through Grade 3 (roughly ages 3–9), and includes skills like letter and word recognition, reading fluency, basic comprehension, number sense, and elementary arithmetic.

FLN is considered the "foundation" of all future learning — without it, a child cannot effectively progress through later grades, no matter how good the rest of the curriculum is.

Why FLN Matters

India has one of the largest school-going populations in the world, but enrollment has not translated into actual learning. Large-scale assessments have repeatedly shown that a significant share of children in upper primary grades cannot read a simple grade-appropriate text or solve basic arithmetic problems. This learning gap compounds over time — children who fall behind in FLN tend to struggle increasingly with every subject built on top of it, leading to disengagement, grade repetition, and eventually dropout.

The National Education Policy (NEP) 2020 explicitly recognized this and stated that achieving universal foundational literacy and numeracy in primary school is the highest near-term priority for the Indian education system — without it, the rest of education policy has limited impact for a large portion of students.

This is the problem our project aims to help solve: giving schools and teachers a reliable, scalable, and personalized way to assess where each child stands on FLN, act on that data quickly, and track progress until every child clears the foundational bar.

Initiatives

Some of the key national and state-level efforts this project aligns with:

  • NIPUN Bharat (National Initiative for Proficiency in Reading with Understanding and Numeracy) — launched in July 2021 under the Samagra Shiksha scheme, with the goal that every child achieves grade-level FLN competencies by the end of Grade 3, by 2026–27. It uses a five-tier implementation structure (national, state, district, block, school).
  • NEP 2020 — the policy mandate that established universal FLN as the top priority for the Indian school system.
  • DIKSHA & UDISE+ — existing national digital infrastructure for teacher resources and student/school data that FLN initiatives are encouraged to build on or align with.
  • State-led missions — several states have their own FLN programs aligned with NIPUN Bharat (e.g., Mission Buniyaad in Delhi, Mission Ankur in Madhya Pradesh), often with localized assessment tools and workbooks.

This project is built to be usable by schools, teachers, and administrators operating within this broader policy ecosystem — generating assessments aligned with grade-wise FLN expectations ("Lakshyas") rather than a generic test.

What This Software Does

The platform is built around personalized, student-specific assessment, not one-size-fits-all testing. Core capabilities:

  • Student Profiling — every student has a profile that tracks their current FLN level, assessment history, and progress over time.
  • Teacher Dashboard — central workspace for teachers to manage classes, generate assessments, scan results, and view analytics.
  • Automatic Question Paper Generation — question papers are generated automatically based on grade level and the student's current FLN level, not just a static template.
    • For a new class/new school with no prior data, the system falls back to a standard question paper aligned with the generic FLN benchmark expected for that grade.
    • Once a student has a profile, future papers are personalized, while still meeting the minimum competency bar defined for that grade under FLN criteria.
  • Print & Distribute — teachers can print a generic class paper or individual, name-tagged worksheets per student.
  • Scan & Auto-Evaluate — after collecting completed sheets, the teacher scans them (via phone camera or a school scanner) and the system evaluates them automatically.
  • Instant Results & Certification
    • If a student clears the FLN benchmark for their grade → they receive a certificate for that grade and progress forward.
    • If a student does not clear it → they receive a detailed analysis of which FLN level they're actually at, along with a scheduled re-assessment date for the appropriate (lower) level.
    • Students who clear a lower-level re-assessment go on to attempt the FLN qualifier for their original grade again — every subsequent paper is generated from their updated, personalized profile.

How It Works (Workflow)

  1. Teacher generates a question paper from the dashboard (standard paper for new classes, or personalized per student once profiles exist).
  2. Paper is printed and distributed to students.
  3. Students take the assessment on paper.
  4. Teacher collects the answer sheets.
  5. Teacher scans the sheets (phone or scanner) and uploads them into the app.
  6. System auto-evaluates the sheet and updates the student's profile.
  7. Teacher gets an instant result:
    • Pass → certificate issued, student advances.
    • Fail → FLN level diagnosis + scheduled re-assessment at the appropriate level.
  8. Cycle repeats until the student clears the grade-level FLN qualifier.

Tech Stack

This project is built on the MERN stack:

  • MongoDB — database
  • Express.js — backend framework
  • React — frontend
  • Node.js — backend runtime

(Specific libraries for OCR/scanning, PDF generation, etc. will be documented as they're added.)

Getting Started

Setup instructions (installation, environment variables, running locally) will be added here as the codebase matures.

git clone https://github.com/vicharanashala/fln.git
cd fln
# setup instructions coming soon

Contribution Guidelines

This is an open-source project — contributions are welcome. Before contributing:

  1. Check open issues or discuss the feature/fix you want to work on.
  2. Fork the repo (or create a branch if you have write access).
  3. Follow the branch naming and PR process below.
  4. Keep PRs focused — one feature or one fix per PR.
  5. Write clear commit messages describing what and why.

Branching & PR Convention

All branches must follow this naming convention:

Type Branch Name Format Example
Feature feat: <name of feature> feat: auto question paper generation
Fix fix: <name of fix> fix: scanner upload crash on android

Process:

  1. Create a branch using the convention above.
  2. Make your changes and commit with clear messages.
  3. Push the branch and raise a Pull Request (PR) against main (or the appropriate base branch).
  4. PRs should reference the related issue (if any) and briefly describe the change.
  5. At least one review/approval is required before merging (process may be refined as the team grows).

License

This repository is open source. (License file to be added — e.g., MIT/Apache 2.0. Update this section once finalized.)