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COVID-19 Lockdown Effects: A Paradigm Shift in Indian Edtech

6 minutes, 16 seconds read

There has been a significant change in the education industry in India in the past couple of years. From syllabus to teaching methods, from enrollment levels to infrastructure available; technology was majorly responsible for this major shift. Educomp, founded in 1994 and one of the earliest Indian edtech, changed academia with multimedia content, computer labs and teacher training. Today, BYJU’S, founded in 2011, is revolutionizing edtech with its m-learning platform. There’s something more that is contributing to the widespread adoption of e-learning platforms.

As the country came to a standstill with a 21-day nationwide lockdown being imposed, online education companies in India sought this as an opportunity to attract students, academicians, schools, colleges, corporates and the media.

Within the first two weeks of the lockdown, many online and edtech players offered their online courses for free — trying to reach as many audiences as possible. The COVID-19 crisis turned out to be an amazing opportunity for edtech to spread its perimeter and reach out to the audience which was earlier ignorant of this sector. But the question is whether the edtech surge is short-term or will it turn out to be a paradigm shift in India.

Why Online Education?

Government awareness programs have shaped the importance of education in people’s minds, which is that education leads to jobs. But the lack of adequate infrastructure, facilities, and teachers have led to decreasing quality of education. Online education is convenient to access, which is why it is gaining popularity amongst the rural population. In places where there is limited infrastructure, many are turning towards online courses, courtesy — access to the internet.

[Also read: What Makes Saas-based Education Technology in India Effective]

While many cannot afford an institutional education, online education has made it monetarily feasible for the population at large. By 2021, $1.96 billion will be the size of the edtech market in India, a KPMG edtech study reveals. In the current crisis where the lockdown has led to massive unemployment, online education concerning skill enhancement has seen an upsurge.

Technology trends in EdTech

From the introduction of hardware such as projectors and computers in the classroom to learning through tabs and laptops at home, the education industry has evolved tremendously. The ideology behind edtech has been to create newer learning experiences keeping with the pace of rapid digitization. 

Gamification has gained significant popularity amongst Indian education service providers as it has made the learning process interesting. Many edtech players have started adopting technologies like Artificial Intelligence and Machine learning which enable teachers and policy makers to get better insights about their students and modify learning methods accordingly. A lot of research is going into technologies like Virtual Reality and Augmented Reality to create interactive learning modules for better understanding of complex subject domains. In case of long-form answers, natural-language processing (NLP) can make the assessor’s job easy by giving detailed and formative feedback.

[Also read: Top 25 Disruptive Augmented Reality Use Cases]

Cloud-based data storage provides convenience to students who can access and share data easily. With the on-going lockdown and social distancing, there could be scope for untapped technologies such as wearable devices and virtual labs which can take learning experiences to another level. 

For instance, Indian edtech startups like Edureka are very serious about customer experience and are taking AI initiatives for Live Chat Analysis and Career Path Research.

[Read Case Study: Customer experience design in Edureka e-learning mobile app]

Opportunities for Indian EdTech amidst the pandemic

Education can be categorized in different segments such as Primary and Secondary education i.e the K-12 segment, Test Prep, Skill Enhancement, and Higher Education. Schools and colleges have been hit quite a bit due to the lockdown as they remain shut till the situation improves. Even though learning has not stopped as teachers have been taking online lessons, will online education replace a traditional one? 

Many edtech experts say that online learning enables students to interact with a larger pool and gives more focus to individual learning. Certainly, technologies can help create innovative and imaginative learning experiences but can they match with learning through human interaction? That is doubtful. However, edtech would be a very powerful aid for teachers to improve the learning process. 

A research by McKinsey states that teachers spend around 20 to 40 percent of their time on activities which could be simplified by automating using current technologies. This time could be optimized by spending on relevant activities focused on student learning. Children are the future citizens of the world. Teachers have a pivotal role in grooming them towards successful personal and professional life. In order to adapt in the post-pandemic world, technology alone cannot bring the change. The learning experience brought in by a teacher is equally important.

The economic slowdown has made the youth cognizant of the unemployment that may hit the world. The upside to this is that the online education industry will see more enrollments in skill-enhancement courses from both rural and urban population. The digital education initiatives will see a monetary boost by the government. This lockdown has enabled people to pursue their passions and take up online tutorials such as cooking, teaching, writing, learning a different language, fitness, learning musical instruments, and other art. This could potentially lead to a thriving passion economy driven by budding entrepreneurs. 

Probable obstacles to Indian EdTech

Edtech will certainly prove to be a booming sector but there are certain challenges on the way. 

Access to internet and bandwidth issues

One of the biggest challenges to the Indian edtech would be accessibility for the population especially in the rural areas. Issues with internet connectivity, bandwidth, hardware might make it difficult to pursue online courses.

Lack of digital literacy

A major part of Indian populace is still digitally illiterate. Especially, the rural population is still not tech-savvy to understand the features of digital devices. Products with simpler UX suitable for the end-user is the need of the hour. Many edtech players still find it difficult to create user-friendly UX that makes technology easy to apply.

Rising competition

EdTech has been making huge progress in the past 10 years and many have recognized its potential to even grow further with the lockdown. The industry is getting crowded with new entrants which makes it difficult for the consumers to remain loyal to one. Subsequently, it is leading to reduced market share for each company.

Investment in advance technologies

This sector definitely has huge potential. However, with the economic slowdown, huge investment in technologies like AI, ML, AR, VR could get affected. There are huge risks in materializing AI projects and might take some time to receive RoI.   

The Bottom Line

Edtech has its pros and cons but there is no doubt that the industry is here to thrive in the long run. This lockdown has proved that the virtual learning systems can operate. Many education boards have understood its potential to grow and will start integrating technology into their syllabus. 

Furthermore, EdTech could significantly improve the quality of educational content and overall learning experience especially for the rural population. For instance, an edtech initiative — EkStep (a non-profit organization) intended to build an advanced, universal, and collaborative platform for K-12 Education space with a focus on rural India. 

Post pandemic, the world will still follow social distancing for some time but the need for human interaction will not diminish but rather see a craving for it. In the short term and the medium term, the edtech industry can reap the benefits of this crisis but to survive in the long run, continuous innovation in technology that does not substitute but rather aid in the classroom learning is needed.

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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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