Application of Big Data Techniques to a Problem

 Bilal hussain Malik 

                                     Application of Big Data Techniques to a Problem

                                       Implications for individual and society 

Drawbacks of Predictive Analytics:

While big data yields interesting insights, it cannot record personal, offline variables like part-time work or family commitments that influence the performance of students. Excessive dependence on algorithms risks unfairly labeling some students as destined to fail, reinforcing stereotypes rather than support.

Implications for Individuals:

Students are growing more concerned about academic surveillance, and they feel uncomfortable being monitored and assessed all the time by computerized systems. A majority of them demanded greater transparency specifically, access to the procedures and standards used in generating their risk scores.

Societal Implications:

If poorly designed, predictive algorithms can reflect or even amplify existing biases. This is a specific risk for neurodivergent students or those with non-traditional learning styles, who will be inappropriately flagged or missed due to atypical behaviours.

Mitigation Strategies:

In anticipation of these concerns, institutions implemented several safeguards:

 Students were able to opt into predictive monitoring functionality, retaining some degree of agency.

Algorithms were regularly audited for bias and explainable AI systems were established to render decision-making transparent.

 Human judgment was maintained through feedback loops, such that automated alerts led to reflective, personalized intervention rather than one-size-fits-all responses.