One of the key advantages of using advanced technology in higher education is its ability to process and analyze large volumes of data quickly and accurately. Traditional methods, such as standardized tests, can be time-consuming and may not always provide an accurate picture of a student’s potential.
Universities can leverage AI and machine learning algorithms to analyze historical student data and predict the likelihood of applicant success at their institution. Additionally, institutional data, behavioral engagement data, and the combination can help predict student retention.
AI Predicts Student Success
AI can identify patterns and trends that may not be immediately apparent to the human eye. For example, an AI system might notice that students who participate in extracurricular activities or have a particular learning style are more likely to succeed academically.
Prescriptive models governed by AI can consider significantly more data faster and more comprehensively than traditional what-if scenario planning.
Educators can then use this information to tailor their teaching methods and resources to support students better. For example, one university researched how machine learning and AI may help the college student pool earlier this year.
University scientists developed neural network models by analyzing student dialogs in the classroom to predict what behaviors may lead to success. After initial analysis, researchers further employed AI to see what behaviors can accurately predict a student's success in STEM (science, technology, engineering, and mathematics) and non-STEM-related courses.
In this study, Big Data helped illuminate a few key aspects that can lead to increased student success and a new vision of how students learn in the first place. Generally, Big Data in the educational context typically takes the form of administrative and learning process data, each offering its promise for academic research.
With AI-driven solutions, universities can understand the relationships between hundreds and thousands of variables to intervene in student behavior. And institutions can do this in real-time, across the student experience.
As data about student interactions change, AI-generated predictions and prescriptions will continue to evolve, providing even better insight and more intelligent recommendations.
AI Predicts Student Retention
Student retention poses a significant challenge to academic institutions. In the US, only about 60% of full-time students graduate from their program, with most of those who discontinue their studies dropping out during their first year.
Academic performance has been identified as one of the most consistent predictors of student retention: Students who are more successful academically are less likely to drop out. Similar research has also highlighted the role of demographic and socioeconomic variables, including age, gender, ethnicity, and socioeconomic status, in predicting a student’s likelihood of persisting.
But a growing body of work has shown the potential of predicting student dropout with the help of machine learning. In contrast to traditional inferential approaches, machine learning approaches are predominantly concerned with predictive performance.
Predictive analytics learns from the data it ingests, sorts through vast amounts of data, and combines that behavioral history in new ways to identify the variables that predict success.
For example, Lovenoor (Lavi) Aulck, a data scientist at the University of Washington, and his colleagues trained a model on the administrative data of over 66,000 first-year students enrolled in a public US university to predict whether they would re-enroll in the second-year and eventually graduate.
They used a range of linear and non-linear machine learning models to predict retention out-of-sample using standard cross-validation procedures. Their model was able to predict dropouts with an accuracy of 88% and graduation with an accuracy of 81%.
Applying predictive analytics at the individual level allows institutions to not only analyze the risk factors most likely to derail a cohort of students but those most likely to derail specific students.
Identifying and targeting applicants who are the best fit for a university and then personalizing all experiences across the student lifecycle will help institutions operate more efficiently, enroll students more likely to graduate, and offer higher-quality experiences.
By utilizing AI to perform time-intensive tasks and better understand and predict performance based on larger and larger data sets, administrative staff can re-focus their efforts on improving student experiences at their schools.
Student Select AI uses natural language processing and machine learning to provide holistic, unbiased, and, most importantly, predictive applicant personality and competency assessments from the application essay or interview transcript.
Going far beyond traditional metrics like test scores, Student Select AI objectively measures candidates’ performance across 17 unique traits, from leadership and communication skills to analytical thinking, proactiveness, and grit.
Is your admissions program ready to move away from traditional success metrics to measure what really matters?
Schedule a live demo today to see first-hand how Student Select AI reduces time-to-decision while providing a holistic, unbiased view of each candidate and ultimately driving better admissions decisions, outcomes, and retention.
