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Best Place To Build

How did this team from IITM quietly build the world's largest edtech platform? | BP2B S1 Ep. 25

In this explosive episode, we sit down with Professor Andrew Thangaraj from IIT Madras—the mastermind behind NPTEL and the IIT Madras BS Program — who is literally revolutionising how millions of students access world-class education.

Andrew Thangarajguest

Jun 20, 202559mWatch on YouTube ↗

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CHAPTERS

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Chapter summaries

Why IITM scaled open lectures before YouTube: the NPTEL vision
Prof. Andrew opens with the core outreach idea: record IIT classes, publish them free, and let anyone access high-quality teaching—even before modern video platforms. He frames scale as a moral and national imperative, citing examples of BS students excelling in competitive exams.
- •Early model: record IIT lectures, host on a portal, free access (pre-YouTube era)
- •Outreach rationale: IITs are hard to enter, but learning can be made accessible
- •Scale mindset: “Whatever we can scale, we should scale”
- •Early proof points of impact (BS students topping GATE DA)
Prof. Andrew’s research and teaching: information theory, probability, statistics
Andrew describes his research focus in information theory and his teaching portfolio. He positions these as mathematically grounded subjects built on probability and statistics.
- •Research area: information theory (probability/statistics oriented)
- •Courses taught: probability, information theory, and statistics
- •Emphasis on theory meeting engineering practice
Shannon’s “magic”: quantifying information and predicting limits
The conversation explains why Shannon’s work was revolutionary: it treated information as a measurable quantity and established fundamental limits for storage and communication. Andrew ties this to real-world technology that now operates close to Shannon’s theoretical bounds.
- •Information becomes a quantifiable entity under Shannon’s framework
- •Fundamental limits for storage, processing, and communication
- •Modern devices/algorithms operate near predicted optimality
- •Information theory as a blend of mathematics, theory, and engineering
Source coding vs. channel coding: compressing and protecting information
Andrew breaks down two key pillars of information theory—source coding and channel coding—using intuitive examples from language redundancy and bit-flips in communication. He clarifies how these ideas enable compression and reliable transmission.
- •Source coding: remove redundancy to store less yet recover meaning
- •Example: predictability/repetition in English speech
- •Channel coding: add structure to correct errors from noisy channels
- •Real-world context: mobile transmission errors and reliability
Electrical engineering explained through the sine wave and control of circuits
Andrew offers a memorable definition of electrical engineering as understanding and controlling sine waves, connecting math/physics to real devices. He anchors EE in controlling voltage/current to build systems ranging from motors to phones.
- •EE as mastering the sine wave (math + physical generation)
- •Core capability: control potential/voltage and current via circuits
- •Applies across scales: generators, motors, transformers, phones
- •EE requires translating between physical reality and abstractions
EEE vs ECE vs EE: why the split blurred and what truly differs
The discussion addresses common confusion between electrical and electronics branches. Andrew explains that distinctions often map to power/voltage regimes, while foundational theory overlaps, leading many IITs to unify into a single EE department.
- •Historical split (EE vs ECE/EC) increasingly unified at IITs
- •Practical distinction: high-power (high V/I) vs low-power electronics
- •Device size and miniaturization differ across regimes
- •Undergrad foundations overlap; specialization deepens later
Specializations within IITM Electrical Engineering and how the field is evolving
Andrew outlines major EE specialization clusters at IIT Madras, from communications/signal processing to microelectronics, circuits/VLSI, and high-power systems. He notes differing maturity levels and how EVs/renewables create new frontiers in power.
- •Key areas: communications & signal processing; microelectronics/devices; circuits/VLSI; high-power systems
- •High-power seen as more mature; comms/SP rapidly maturing
- •New drivers: EVs, renewables, solar, power electronics innovation
- •IITM has many specializations; Andrew suggests fewer broader buckets can suffice
Is IITM Electrical “hard”? Grading culture, abstraction, and why Fourier confuses people
Andrew contrasts past harsh grading norms with today’s more supportive environment. He explains that EE feels difficult because it demands comfort with abstraction and switching between mathematical models and physical intuition—Fourier transform being a classic hurdle.
- •Earlier era: very strict grading; today: more generous/supportive culture
- •EE difficulty comes from mixing physics + math + abstraction layers
- •Circuit theory and nonlinearity can be conceptually hard
- •Fourier transform as a central but highly abstract EE concept
NPTEL origin story (2000–2010): recorded IIT classes as a national teaching resource
Andrew credits early leaders and describes NPTEL’s original mission: democratize access by recording full IIT courses and distributing them widely, including via DVDs. As internet improved, NPTEL’s reach expanded further, eventually moving strongly onto YouTube.
- •NPTEL proposed/funded around 2000; major credit to early champions
- •Model: full classroom recordings, posted free for national access
- •Pre-MOOC era; adoption by colleges and faculty as teaching support
- •Internet/3G era boosted video consumption and scale
Adding certification and proctored exams: making online learning mainstream in India
Andrew explains how learner demand drove NPTEL toward certification, and why IITs rejected purely online exams for India’s context. Proctored exams created credibility comparable to traditional education, enabling wider institutional acceptance.
- •Learners asked for proof of learning: a recognized certificate
- •Decision: credibility requires proctored (center-based) exams
- •Proctoring explained: supervised exam environment reduces cheating
- •MOOC hype vs. practical Indian constraints shaped the design
From one pilot to massive scale: NPTEL MOOCs, enrollment funnel, and national adoption
The first certified course (PDSA) demonstrated huge interest but also a steep completion funnel. Over time, the model scaled dramatically to hundreds of courses per semester and nearly a million exam registrations, overtaking legacy exam scales in participation.
- •2014 pilot: ~1 lakh enrollments → ~3,000 exam-takers → ~1,000 certificates
- •Steep funnel is normal: many enroll, fewer persist to exams
- •Scale today: ~800 courses/semester; ~30 lakh enrollments; ~9.8 lakh exam registrations
- •Pan-IIT contributions with IITM coordinating operations
SWAYAM and credit transfer: building a national MOOC-to-degree bridge
Andrew defines SWAYAM as the ministry’s MOOC portal that expanded the certification model beyond technology and enabled systematic credit transfer. This reduced the friction of university-to-university MOUs and made online courses part of mainstream college pathways.
- •SWAYAM launched (2017) to expand certified online learning across domains
- •Credit transfer becomes easier because the ministry framework standardizes acceptance
- •Replaces complex bilateral MOUs for course recognition
- •IITM CODE runs SWAYAM portal operations for the ministry
Why IITM moved from courses to degrees: limits of JEE, access, and “scale what you can”
With NPTEL proving many non-IIT learners could excel, the conversation turns to the inequities of JEE coaching and the mismatch between entry filtering and actual capability. Andrew frames the BS degree as scaling curriculum access while acknowledging campus life is harder to replicate.
- •JEE coaching cost and intensity exclude many talented students
- •NPTEL toppers show capability exists beyond IIT entry filters
- •IITs get large funding share but serve tiny UG enrollment—pressure to scale what’s scalable
- •Campus peer ecosystem is hard to scale; curriculum and assessment can be scaled
Inside the IIT Madras BS model: qualifier → foundation → diploma → degree (skills-first design)
Andrew explains the BS program architecture that shifts filtering from entry to progression. Students try a low-cost foundation stage, build core readiness, then face a rigorous diploma stage emphasizing hands-on skills and project evaluation before moving to the degree stage.
- •BS chosen as a differentiated UG offering; credit to key institutional leaders
- •Progression model: larger intake, then filtering through stages rather than at entry
- •Foundation: ~8 courses (math/stats/programming for DS); low cost (~30K) to test fit
- •Diploma: toughest filter; skills-first (projects, vivas, live changes) before deeper theory
Affordability, outcomes, and redefining “quality”: transformation stories and GATE ranks
The program’s pricing and fee support aim to include low-income learners while maintaining rigor through assessments and filtering. Andrew argues quality should be measured by transformation and employability, citing remarkable learner journeys and top GATE outcomes.
- •Total fees: ~3.2L (Data Science) and ~5.5L (Electronic Systems) with substantial fee support
- •Large share of learners from lower income brackets (notably <5L and <1L categories)
- •Quality re-framed: not just selecting the best, but enabling major capability gains
- •Outcome signals: BS learners’ peer communities, IITM alumni status, GATE top ranks (including an AIIMS doctor)
What’s next for CODE: more degrees, deep-tech upskilling, and the IITM operating advantage
Andrew discusses future program considerations (demand scale, lab constraints, non-engineering opportunities) and positions CODE as the umbrella for IITM’s external education. He contrasts IITM’s nation-building incentives and in-house execution with venture-funded edtech pressures.
- •Future expansions: careful selection based on employment demand and scalability
- •Challenges in core engineering due to lab requirements; hybrid campus labs for electronics
- •Non-engineering opportunities: economics/finance/commerce via partnerships
- •CODE scope: conferences, executive education, Web MTech, bespoke corporate training in deep tech
Back to campus: Engineering Physics vs EE, student experience, and quick BS vs BSc clarification
The conversation closes with a brief return to on-campus academics: EP overlaps heavily with EE and EP students often excel in mathematical maturity. Andrew shares his own IITM student journey and ends with a simple clarification: BS is a four-year degree while BSc is typically three years.
- •EP vs EE: substantial curriculum overlap; EP students often strong in math/concepts
- •Andrew’s IITM student years (1994–1998): personal growth and diversity of peers
- •CODE as outreach umbrella beyond BS degrees
- •BS vs BSc: commonly 4-year vs 3-year structure; exit options exist within the BS pathway

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