Application of Big Data Techniques to a Problem

Bilal Hussain Malik

                                     Application of Big Data Techniques to a Problem   


                                             Application of Big Data Techniques

Problem Identified:

The highest priority problem was the 28% dropout rate of first-year students from online STEM courses, highlighting the need for intervention at an early stage as well as enhanced mechanisms for support to the students.


Data Collected:

To address this issue, some learning trends through digital learning were tracked, including:

• Frequency of logins to the Learning Management System (LMS)

• Long-term trends in quiz scores

• Discussion forum usage

• Viewing duration of video lessons

Assignment hand-ins timeliness and consistency

Data Mining Techniques Utilized:

Performance declines were forecasted with the aid of regression analysis according to patterns of behavior.

NLP techniques detected discussion forum activity for indicators of stress, frustration, or disengagement using sentiment analysis.

Implementation Strategy:

•At-risk students received individual messages, e.g., tutoring invites, assistance, and helpful links.

•Teachers were automatically informed about high-risk students for one-on-one contact and support.

Results:

The strategic use of big data yielded measurable success:

•The rate of drop-out fell from 28% to 17% within a single semester.

•Course completion rates improved by 21%.

Student feedback reported that they felt more supported and engaged during the course.

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