Education and edtech: ML domain track
You already think in learning goals, assessments, and learner variation. This track orders the shared Road to ML spine for education and edtech-style analysis. It teaches educational methods and evaluation habits. It is not certified teacher training. It does not guarantee classroom outcomes or a hire.
Core idea. Keep instructional judgment. Add Python, supervised learning, careful splits, and explainability. Methods live in Modules 00–25.
Who this is for
- Teachers, instructional designers, and education students exploring ML literacy
- Edtech builders who need predictors on assessment or engagement tables
- Switchers who want Data Scientist or Data Analyst emphasis without hype
What you already bring
- Sense for learning objectives and assessment design
- Respect for noisy human labels and incomplete attempts
- Stakeholder questions that need plain-language error talk
ML problem types you will meet
- Classification of mastery, risk, or item outcomes from features
- Regression on scores or time-on-task style continuous targets
- Imbalance when “at risk” labels are rare
- Optional NLP later for open responses (only after Module 05 is solid)
- Careful evaluation so school, cohort, or attempt leakage does not fake gains
Gaps this track closes
- Module 01
- Modules 02–05
- Module 04 as a usual first modeling step
- Module 19 when LMS data is tabular
- Module 21
- Optional Module 12 for text-heavy tasks after baselines
Related resources
- Ethics in ML. Learner data needs care. Educational methods only.
- Stakeholder Communication. Talk about error with teachers and product owners.
- Causal Inference Guide. When the question is intervention, not only prediction.
- Imbalanced Data Cheatsheet. At-risk labels are often rare.
- Model Interpretability. Deeper reading beside Module 21.
- Data Validation. LMS and assessment tables need honest checks.
Role emphasis (not destiny)
| Emphasis | Role | Why |
|---|---|---|
| Primary | Data Scientist | Models plus evaluation match learning analytics questions |
| Alternate | Data Analyst | Strong when reporting and SQL matter more than new models |
See Career Paths and Career Roadmap Guide. Treat times as emphasis maps only.
Intensity maps
Research support (tier B)
- Module 00 if needed
- Module 01
- Module 02
- Module 04
- Module 05
- Module 21
- One Module 16 project you can defend without “personalized AI” marketing
Optional later: Module 20 for rare risk labels. Module 12 only for text after Module 05.
Job-oriented study (tier B)
- Complete Research support
- Add Module 19 and Module 07
- Add Module 03 if scores are continuous
- Keep deployment modules for later unless the product truly ships models
Ordered study checklist
- Pick intensity map
- Finish Module 01 with an assessment-style table (public or own)
- Finish Modules 02, 04, and 05 with a split by learner, class, or attempt wave
- Finish Module 21 and explain one false alert to a teacher collaborator
- Document limits: not a teaching credential, not licensing, no guaranteed learning gains
Start here
Open Module 00 README if you need math or environment help. Otherwise open Module 01 README.
Honesty and traps
- Educational content only. Not certified teacher training or licensing.
- Do not treat vendor “AI tutor” marketing as a methods curriculum.
- Privacy: do not publish identifiable student data in public repos.
- A high AUC on leaked classroom data is not classroom truth.
Try next: Open Module 05: Model Evaluation and Optimization early. Education stakeholders will ask how you split and scored.