Application of Big Data Techniques to a Problem

 Bilal Hussain Malik 

                                     Application of Big Data Techniques to a Problem


                                     Application in Business, Science and Society


Business Application:

Institutional performance is enhanced by leveraging big data to raise student retention rates—a prime indicator of success for education providers. Greater retention reduces the rate of tuition refunds and enhances institution performance-based funding eligibility, with direct consequences for financial sustainability.


Scientific Application:

The high-quality information collected is of great use for research, particularly learning analytics and the effectiveness of online pedagogical strategies. Trend analysis on the behavior of students can be done by analysts to make pedagogical strategies more accurate and improve learning results.


Societal Application:

Big data facilitates greater educational equity by making early, personalized intervention with rural or underrepresented students possible. Early detection of learning deficiencies or disengagement allows institutions to correct situations before students fall behind, redressing imbalances of access and achievement.

Prospective Future Applications:

•AI-Powered Personalized Learning Routes: Artificial intelligence would respond to individual learning requirements, rate, and interest by adjusting educational content.

Monitoring of Mental Health: Sentiment analysis of students' written communication would help in detecting early warning signs of emotional distress, invoking proper mental health interventions in a timely manner.

Infrastructure Needed in Technology:

• Learning Management Systems (LMS): Platforms like Moodle or Canvas that possess real-time analytics functionalities and user interaction monitoring.

• Cloud-Based Data Warehousing: Centralised data processing and storage systems to deal with huge student data securely and efficiently.

• Machine Learning Tools: Algorithms capable of identifying patterns in students' behaviour and predicting outcomes.

Data Visualisation Tools: Tools like Power BI or Tableau to graphically represent challenging data in simple and actionable manners.

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