Summary

Explore how predictive analytics can enhance student outcomes. This article discusses the role of data in identifying at-risk students and improving educational interventions.

Predictive analytics can significantly accelerate knowledge assimilation in students and improve their success rate.

The predictive algorithms can be leveraged to uncover the vulnerabilities in students’ academic progression and facilitate adequate and proactive remedial interventions. Machine learning (ML) techniques, statistics, big data, and AI-driven algorithms are combined to predict future trends impacting learner outcomes.

Read on as we take you through ten ways predictive analytics can enhance student outcomes.

Table of Contents:

10 Benefits of Implementing Predictive Analytics for Students

A higher education institute can rely on insights into students’ performance through predictive analytics to offer targeted and customized interventions. This is why 89% of US universities already invested in predictive modeling to boost student outcomes. 

Here’s a snapshot of the ways predictive analytics can enhance student outcomes:

1.  Enhances the Efficacy of the Curriculum

Predictive analytics evaluates student performance data to identify curriculum areas that need improvement. Institutions can refine and update curricula to strengthen conceptual understanding by pinpointing topics where students struggle.

2. Offers Formative Feedback

Predictive analytics and machine learning enable the creation of dynamic student dashboards that deliver timely formative feedback on assessments. By analyzing students’ performance data, these dashboards can recommend suitable courses and learning materials tailored to individual needs.

Through in-depth analysis, educators gain insights into students’ learning preferences and identify areas where they struggle. This data-driven approach not only enhances student motivation but also helps them focus their efforts effectively, which ultimately leads to improved academic outcomes.

3.  Helps in the Strategic Segmentation of Learners

Demographics and other broader characteristics are sidelined to group learners based on subtle traits like academic preferences, usage of particular resources, learning inclinations, etc. Such segmentation proves useful for recommending curated learning content and forming focused study groups.

Also Read: Faster, Better Learning with Hurix’s Powerful Platforms & Tools, Designed for Students

4.  Facilitates Personalized Learning

Predictive analytics can be leveraged for building data-analytic models that dissect relatable patterns in individual learning efforts. This is beneficial for figuring out the learning gaps in students and shortcomings in their approaches. Further, the anomalies in the academic curriculum also become apparent.

The insights drawn from analytic models can be used for customizing learning modules in alignment with individual students’ learning styles and academic goals. This personalization of learning pathways enriches students’ learning experience and enhances course completion rates.

5. Paves the Way For Better Student Retention

The predictive models help educational institutes get actionable insights into the engagement levels of enrolled students.

The models highlight trends in students’ academic performances and help segregate those who are at risk. Consequently, faculty members can intervene at an appropriate time to offer support, mentoring, and counseling, which can considerably improve students’ retention rates. 

Incidentally, Utah’s Western Governors University succeeded in increasing the graduation rate by 5% for its 4-year undergraduate course from 2018–2020 through predictive modeling. This approach is also useful for minimizing the risk of a backlog that students might face in their learning journey.

6. Improves Policy-Related Decision-Making

Predictive analytics empowers higher education institutions to make informed policy decisions by forecasting future scenarios based on comprehensive data insights.

By leveraging analytics, institutions can effectively address issues such as the DFWI (Drop, Fail, Withdraw, Incomplete) indicator, enabling strategic adjustments to policies.

For instance, the University of North Texas utilized data visualization and analytics to refine procedural policies, ultimately boosting retention rates and improving educational quality. This initiative enhanced student outcomes and yielded substantial cost savings, totaling $450,000 for the university.

7.  Allows Student-Oriented Recommendations in More Informed Manner

Educators can monitor the performance of students in a sustained manner through digital surveillance and analytical methods.

The big data that is collected can be modeled using AI to predict the students’ success or failure probability rates in summative assessments. Predictive analytics in big data helps teachers identify learners who need more attention. Accordingly, such students can be recommended relevant services or content to ensure their success.

8.  Makes Learners More Responsive

Predictive analytics can uncover trends, which can increase learners’ responsiveness to teachers’ attentive care. 

This way, educators can decide on corrective measures based on predictive models to improve learners’ overall performance. Eventually, they can make more conscious decisions regarding the use of peer reviews, micro-learning videos, and weekend classes to enhance learners’ engagement.

9. Creates Avenues For Continuous Improvement

Educational institutions can embark on a path of continuous improvement by systematically gathering and analyzing relevant data from students, faculty, and administrators.

This data-driven approach enables institutions to enhance departmental efficiency through proactive strategic planning.

For example, Orion Starve WPI implemented self-service dashboards powered by predictive analytics, empowering departments to assess and optimize their operations autonomously. This capability allows departments to make informed decisions through ad hoc analyses, ultimately improving student outcomes.

10.  Enhances Career Counseling and Placement Services

Predictive analytics can revolutionize career counseling and placement services by analyzing students’ academic performance, skills, and interests.

By predicting potential career paths based on data insights, institutions can offer personalized guidance to students and match them with suitable internship opportunities, job placements, or further education options.

This proactive approach not only increases student satisfaction but also boosts post-graduation success rates, ensuring students enter the workforce well-prepared and aligned with their career aspirations.

Also Read: A Guide to Predictive Analytics for Business Excellence

Way Forward

Predictive analytics is extremely effective in forecasting the likelihood of specific events in the academic space. 

Educational institutes can leverage insights from predictive AI to identify trends, forestall issues, enhance the efficacy of feedback methods, and recommend the right course of action for learners. 

In this regard, note that Hurix Digital has a team of AI and ML experts who partner with educational institutions. Together, we use predictive analytics to segment learners, create personalized learning content, recommend tailored training, and provide timely support. We aim to help educators maximize student potential and improve success rates.

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