Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Educational Research shows how machine learning expands what education researchers can study and how rigorously. You learn to apply AI to learning and assessment data — modelling outcomes, mining patterns in how students learn, analysing text and interaction logs, and supporting educational-data-mining and learning-analytics research. The course keeps research rigour central: validity, reproducibility, bias, and the ethics of studying learners. Connecting methods to real research questions, it strengthens evidence in education. You finish able to apply AI to an educational-research problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in educational research — using machine learning to analyse learning data, model outcomes and strengthen evidence in education research.
📋 Course Objectives
1. Apply machine learning to learning and assessment data.
2. Model educational outcomes and patterns.
3. Analyse text and interaction logs.
4. Uphold validity, reproducibility and ethics.
5. Connect methods to research questions.
👥 Who Should Enroll?
• Education researchers and academics
• Learning-analytics professionals
• PhD scholars in education
• Students of educational research
🚀 Key Learning Outcomes
• The ability to apply AI in education research.
• An educational-data-mining perspective.
• A rigour-first research approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Research Questions and Study Design
• Where AI genuinely helps an education researcher and where it merely accelerates error
• Quasi-experimental designs when randomisation is impractical in classrooms
• Construct validity: measuring learning rather than measuring engagement
Learning Analytics Data and Ethics
• Clickstream, assessment and LMS data: what each can and cannot evidence
• Consent, minors, and institutional review requirements
• Anonymisation limits when timestamps and trajectories re-identify students
Modelling Learning
• Knowledge tracing and mastery estimation
• Clustering learner trajectories and the instability of unsupervised groupings
• Natural language processing on open responses and reflective writing
• Text mining literature at scale for systematic reviews
Validity, Bias and Replication
• Algorithmic bias against under-represented learner groups
• Overfitting to a single cohort, institution or platform
• Pre-registration, open materials and reproducible analysis pipelines
Communicating Findings Responsibly
• Reporting effect sizes and uncertainty rather than model accuracy alone
• Writing methods sections that another researcher could actually rerun
• Translating findings into practice without overclaiming causality
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
