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EdTech

Personalize learning. Automate grading. Keep students engaged.

We build learning management systems, adaptive learning engines, assessment platforms, and student engagement tools for education companies and institutions. The software that improves completion rates, reduces instructor workload, and makes learning actually work for each student.

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45%

Completion rate lift

3x

Engagement increase

Overview

Your learners are dropping out because the platform can't adapt

EdTech software development at 1Raft focuses on the three areas that most directly impact learning outcomes: personalization, assessment efficiency, and retention. We bring patterns from 100+ products across adjacent industries to build adaptive learning engines, AI grading systems, early-warning tools, and content generation platforms - each engineered to drive measurable improvement in completion rates and learner engagement.

Most eLearning platforms are glorified file servers - they host videos and PDFs, track completion, and call it a day. The result: 85% of online courses have completion rates below 15%, instructors spend more time grading than teaching, and students who struggle get no help until they fail.

Our adaptive learning systems adjust content difficulty in real time based on student performance, assessment platforms grade open-ended responses with 94% accuracy, and engagement engines predict dropout risk 3-4 weeks before it happens.

Every product we build integrates with existing LMS platforms - Canvas, Blackboard, Moodle, and custom systems - via LTI, xAPI, and standard APIs. We work with your existing content library and student data. No platform migration required.

Experience Signal

1Raft builds adaptive learning platforms, AI assessment systems, early warning tools, and LMS integrations for coding bootcamps, universities, online degree programs, and corporate training providers. Our engineering draws on patterns validated across 100+ products in adjacent industries.

45%

Completion rate lift

3x

Engagement increase

See case study

Industry Pain Points

What's broken in edtech

01

Online course completion rates average 5-15% because one-pace-fits-all delivery loses students who are either bored or overwhelmed

02

Instructors spend 10-15 hours per week on grading and feedback, leaving minimal time for curriculum development and student interaction

03

At-risk students are identified only after they fail an exam or stop showing up - by then recovery is expensive and unlikely

04

Content creation takes 200+ hours per hour of polished eLearning, making it impossible for most institutions to keep curriculum current

05

Learning analytics are limited to completion rates and quiz scores, giving no insight into what students actually understand or where they struggle

Solutions

Problems we solve in edtech

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Adaptive Learning Engine

Adjusts content difficulty, sequence, and format based on each student's performance, learning pace, and engagement patterns. Students who grasp a concept quickly move forward; those who struggle get alternative explanations and additional practice.

02

Automated Assessment and Grading

Grades open-ended responses, essays, and code submissions with detailed feedback. Handles rubric-based evaluation, plagiarism detection, and feedback generation. Frees instructors from repetitive grading while maintaining assessment quality.

03

Early Warning and Intervention System

Tracks engagement patterns, assignment completion, assessment performance, and login frequency to predict dropout risk. Triggers automated nudges and alerts instructors to intervene before students disengage completely.

04

Content Generation and Curation

Generates practice problems, quiz questions, study guides, and explanatory content from existing course materials. Curates supplementary resources from open educational content. Reduces content creation time by 60-70%.

05

Learning Analytics Dashboard

Goes beyond completion rates to show concept mastery, skill gaps, engagement trends, and cohort comparisons. Helps instructors and administrators understand what students actually know and where curriculum changes will have the most impact.

Use Cases

Real-world use cases

Adaptive Learning Platform for a Coding Bootcamp

Problem

A coding bootcamp with 2,400 annual students had a 32% completion rate. Students with prior experience found the curriculum too slow, while beginners got lost by week 3. Instructors couldn't personalize across 200-student cohorts.

What we built

We built an adaptive learning engine that assesses each student's skill level at enrollment, adjusts content difficulty and pacing in real time, and provides alternative explanations when students struggle. Added AI-generated practice problems tailored to each student's weak areas.

Result

Completion rate improved from 32% to 61%. Student satisfaction scores increased 1.4 points. Time-to-job-ready decreased 18% for advanced students. Instructor time spent on individual remediation dropped 55%.

Automated Assessment for a University Writing Program

Problem

A university writing program had 3,200 students per semester across 80 sections. Each student submitted 6 essays per term. Teaching assistants spent 20+ hours per week grading, with inconsistent feedback quality across sections.

What we built

We built an AI grading system trained on 5 years of faculty-graded essays with rubric scores. The system evaluates thesis clarity, argument structure, evidence use, and writing mechanics. Provides detailed paragraph-level feedback and suggested revisions.

Result

Grading time per essay dropped from 18 minutes to 4 minutes (AI pre-grade + TA review). Feedback consistency across sections improved 72% as measured by rubric score variance. Student revision quality improved measurably - second drafts scored 0.6 points higher on average.

Early Warning System for an Online Degree Program

Problem

An online degree program with 8,000 active students had a 44% first-year attrition rate. Student advisors managed caseloads of 400+ and couldn't proactively identify at-risk students until they stopped submitting work.

What we built

We built a predictive model using LMS engagement data, assignment submission patterns, discussion forum activity, and assessment scores. The system assigns risk scores weekly and triggers automated nudges for medium-risk students and advisor alerts for high-risk students.

Result

First-year attrition decreased from 44% to 31%. Advisors focused on the 15% of students who needed intervention most, instead of reacting to the 44% who had already disengaged. At-risk students contacted within 48 hours of risk escalation had a 3.1x higher retention rate.

Our Approach

How we approach edtech projects

1
Phase 1· Weeks 1-2

Learning Experience Audit

We analyze your curriculum, learner data, completion rates, and instructor workflows. We identify where students are dropping off, what content isn't working, and where instructor time is being wasted on tasks AI can handle.

Deliverables

  • Learner journey analysis with drop-off points and engagement patterns
  • Instructor time audit quantifying hours spent on grading, admin, and student support
  • Prioritized opportunity list ranked by learner outcome impact and implementation speed
2
Phase 2· Weeks 3-4

Product Design and LMS Integration Planning

We design the product with your instructional design, faculty, and IT teams. Every integration point - LMS, SIS, content library, assessment tools - is mapped before build.

Deliverables

  • Product design validated by instructional design and faculty reviewers
  • LMS integration specifications (LTI, xAPI, custom API)
  • Data architecture for learner profiles, content metadata, and analytics
3
Phase 3· Weeks 5-10

Build, Integrate, and Pilot

We build in sprints and deploy to a pilot course or cohort first. Real student interactions validate the product before institution-wide rollout.

Deliverables

  • Working product deployed in pilot course with real students
  • Learning outcome metrics from pilot cohort
  • Iteration backlog based on student feedback and instructor observations
4
Phase 4· Weeks 11-14

Institution-Wide Rollout and Optimization

We roll out across courses and programs with curriculum-specific configuration. AI models improve continuously as more student interaction data feeds into the system.

Deliverables

  • Full deployment across target courses and programs
  • Learning analytics dashboard for instructors and administrators
  • Quarterly optimization plan tied to completion rate and outcome targets

Outcomes

Measurable outcomes

40-80% improvement in course completion rates through adaptive pacing and personalized content
60-75% reduction in instructor grading time through AI-powered assessment and feedback
25-35% decrease in student attrition through early warning and proactive intervention
50-70% faster content creation through AI-generated practice problems and supplementary materials

Pattern Transfer

1Raft built churn prediction models for a streaming media client before applying the same engagement-scoring approach to student dropout prevention. Both problems require identifying disengagement signals from behavioral data and triggering the right intervention at the right time. The domain changed; the pattern transfer was direct.

Services

Services for edtech

Proof

EdTechMusic Learning App
50,000+

Active learners

4.8/5

App rating

Read case study

Frequently asked questions

Projects range from $50K-$200K. An adaptive learning module that plugs into your existing LMS starts around $50K. A full custom LMS with assessment, analytics, and content tools runs $120K-$200K. We provide a fixed estimate after a strategy session.

Next Step

Every semester with 40% dropout rates is another semester of wasted potential and lost tuition.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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