Closing learning gaps with predictive analytics

What if you could predict the future and adjust your choices to match your needs? Today, organisations rely on predictive analytics to understand their target audience and achieve better results. Simply put, predictive analytics uses past data to predict future trends and patterns. For instance, insurance companies use predictive analytics to design personalised and competitive policies while maintaining portfolio stability and profitability. In healthcare, data analytics helps analyse patient records, medical history, demographics, and past hospital visits to create models that predict admissions and readmissions. Similarly, predictive analytics can identify individual learning needs and make education more personalised and effective.

According to Prof Mahadeo Jaiswal, Director, IIM Sambalpur, predictive analytics plays a significant role in identifying learner needs and constructing customised educational experiences by analysing data including past academic performance, engaging in classroom activities and metrics from Learning Management Systems (LMS). It can also help identify areas where a learner excels or struggles by predicting potential challenges or dropouts, through these data faculty can provide timely support and counselling to address issues before they escalate. “These analytics can recommend specifically crafted learning courses that align with each student’s career aspiration, ensuring a smooth learning journey. Moreover, predictive analytics are often used to offer instant and targeted feedback, providing learners with authentic instructions to stay on track and improve simultaneously,” he said.

Artificial Intelligence (AI) is essential to predictive analytics, enabling the analysis of vast amounts of data to reveal hidden patterns and relationships that traditional methods might miss. AI has become a vital part of various sectors, including education, where its influence is revolutionising teaching methods, student learning, and administrative processes. In administration, AI-driven tools are streamlining tasks like grading, easing educators’ workloads, and ensuring students receive prompt feedback.

In fact, predictive analytics also help educators track progress and intervene early to support students at risk of falling behind, admitted Rohit Gupta, CEO & Co-founder, College Vidya. “Learning analytics provides real-time dashboards and predictive insights into student performance. Educators can monitor attendance, participation, and grades to identify at-risk students early. Timely interventions, such as personalised coaching or additional resources, can then be implemented to help these students get back on track,” he said.

Implementing real-time feedback loops in educational settings is also very beneficial for both educators and students. For students, this can be done by examining the process, providing immediate insights into students’ performance and fostering continuous improvement by providing authentic instructions. This customised approach encourages engagement and motivation, by timely acknowledging their progress and providing tailored resources to address individual weaknesses, said Prof Jaiswal. However, he also warned about the ethical challenges in the collection, analysis, and use of student data. “One major factor is ensuring that student’s personal and academic data are collected with informed consent and used strictly for educational purposes. However, misuse or overreach, such as using data for non-educational objectives, can erode trust and raise ethical concerns. There is a risk of bias in data analysis, which may lead to unfair treatment among the students. To address these drawbacks, institutions must prioritise transparency by clearly communicating what data is required, why it is required and how safe that data is with the institution. Establishing robust data governance frameworks adhering to legal and ethical standards such as GDPR or similar regulations can minimise the risk of data misuse,” he said.

Gupta also highlighted the importance of institutions in implementing robust data governance frameworks. “They should seek informed consent, anonymise data, and comply with regulations like the Digital Personal Data Protection Act (DPDPA). Transparent communication about data usage and offering students control over their data further builds trust,” he said.

AI as teacher

IIM Sambalpur has introduced AI as a faculty. This AI, with machine learning, deep learning, and data analytics, essentially becomes the professor. All the information is fed into the system, and it begins the session. If the class starts at 9 am, the first 15 minutes are for individual introductions and initial discussion. Afterwards, a quiz is taken by the AI in the form of a poll, testing if the students have understood the material. With these insights, educators can intervene early by offering personalised support, such as one-on-one mentoring, targeted resources, or tailored assignments to address specific challenges.