EdTech

Market Outlook:

AI-powered personalization harnesses the power of data to allow students to pursue a guided, hyper-personalized line of study of their choice in depth and help individuals to take training programs to excel in their professional careers. Dynamic AI-powered personalization is capable of understanding, interpreting, and predicting student behaviour across interactions on any platform and can capture the exact intent of students in real-time.  

Data Analytics and Machine Learning can examine a student’s education trail, also not only helping in comprehending this uniqueness but also influencing it by creating an individually adapted study plan with relevant pedagogical proposals and contents procedure, to achieve their specific learning goals. It is like having a custom-made tutor for each student.

Use-Cases

Predictive Modelling:

Predictive modelling refers to a statistical technique used to ascertain the probability of a pupil performing in an enrolled program. Predictive learning models are developed out of these virtual profiles to generate insights into students in the future. Through business data analytics, you can identify students struggling or at risk to bring them back on track.

Edtech Analytics:

Ed-tech platforms make it easy and accessible to gather, aggregate, and analyze activities. Through the power of e-tech analytics, instructors can explore the online behaviours of their students, monitor the time they spend in the virtual learning environment or submit their coursework.

Personalized Training:

Customized recommendations can be made through machine learning (ML). Adaptive learning systems use neural network algorithms to create dynamic course structures by gathering data from peer learners, making recommendations on course materials and time that should be spent on completing a course. Such systems are created to collect data, analyze, create personalized content, and make decisions to optimize a training module.

Data Consolidation:

Gathering all insights from disparate sources throughout the Edtech platform used, cleaning the data, and combining it all in a data lake. With all insights in one place, the platform can have a 360-degree view of its usage and overall business. The consolidated data can be transformed for useful reporting and analytics. Data consolidation helps to provide business insights and reliable business intelligence.

Benefits

Enhanced

Customer Insights

Interactive personalized

Learning experience

Adaptive

learning

Track and

analyze progress

Case Study

Problem:

Edtech company uses multiple platforms to manage their training and general business activities. The data did not reside in one place to perform a holistic analysis of data. Each tool had to be accessed separately to get relevant insights.

Solution:

EdTech Company consolidated the data from different sources in a data lake. New visualization tools were also recommended and the best POCs for each visualization tool. Data Pilot is currently providing data engineering services to EdTech Company.

Results:

Data Pilot optimized EdTech Company's data collection. We enhanced EdTech Company's ETL pipeline, using a new tool that unlocked data fetching from a source that was completely inaccessible through the previous system.

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