As per the most recent report, 853 colleges received applications from 1,106,777 unique applicants through January 17, 2022, an increase of 13.2% over 2019–20 (977,914).
Looking at these figures, it can be concluded that you have a sizable applicant pool, though not all of them will be fit for your institute as per your set standards. It is very important to look for quality admissions rather than drooling over quantity!
Quality admissions make sure the institution can draw in and enrol top-notch students who can meet the academic requirements of the institution. This is crucial because the calibre of the student body can have a big impact on the institution’s overall intellectual and academic climate.
Quality admissions also guarantee that a diverse and inclusive student body will continue to exist. This can be done by taking into account a number of factors like academic ability, extracurricular accomplishments, and diverse backgrounds.
But how will you sort these quality applications among this sizable pool of applicants?
Either you or your team can spend hours on Excel sheets going through thousands of applications and supporting materials like transcripts, test results, essays, and letters of recommendation.
Or you can leave everything up to technology. Let’s now examine how automation can make shortlisting simple for you.
Assets to automate candidate shortlisting for quality admissions
There are a number of technologies that universities can use to automate the process of short-listing candidates for admission.
But first, you need to remove all the junk leads so that you can focus on quality leads. Through a robust education CRM, you can do this automatically by:
- Verifying the contact information (phone, email, address) through OTP generation
- Lead score helps you prioritize leads based on their likelihood of converting and can assist in the lead verification process.
- Checking for duplicate leads and automatically removing them from the database.
- Easily identify which leads are from a high-value source.
After performing this top-of-the-funnel verification, you now focus on shortlisting quality candidates for your institute.
Some of the ways listed below can assist you in completing this task with minimal manual effort.
Application Management System (AMS)
An AMS, or Application Management System, is software designed to speed up and automate the process of looking over and assessing applications from potential students.
An AMS can help universities shortlist candidates in a number of ways:
- Automates the collection and organisation of application materials: An AMS can collect, store, and organize all of the materials that are needed to review an application, such as transcripts, test scores, essays, and letters of recommendation. This can make it easier for admissions committees to access and review all of the necessary materials in one place.
- Provides tools to evaluate and rank applicants: An AMS include tools or features that allow admissions committees to evaluate and rank applicants based on specific criteria, such as grades, test scores, and other qualifications. This can help universities quickly and accurately identify the most qualified candidates for further consideration.
- Simplifies the communication and review process: An AMS provides a centralized platform for admissions committees to communicate and collaborate on the review process internally. This can help to streamline the review process and ensure that all committee members have access to the same information.
Data Analytics and Artificial Intelligence
Data analytics is the process of examining data sets in order to find trends and draw conclusions about the information they contain, whereas artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. Both are useful tools for universities in the process of shortlisting candidates for admission.
Here are a few ways that data analytics and artificial intelligence can help with admissions:
- Identify patterns and trends: By analyzing data from past applicants, universities can identify patterns and trends that may be indicative of success in their programs. For example, if you have data on candidates from certain cities who prefer your institute or certain programs, you can market these programs aggressively in that region. This information can be used to help shortlist candidates who are most likely to succeed at the university.
- Improves the fairness and objectivity of the admissions process: AI systems can be designed to be unbiased and objective, which can help to reduce the potential for human biases or subjectivity to influence the admissions process. This can help to ensure that admissions decisions are based on objective criteria, rather than subjective opinions or prejudices.
- Automate the review process: AI can be used to automate the review process for applications, allowing universities to evaluate a large number of applications. For example, an AI system might be able to extract and analyze data from applications, such as grades, test scores, and other qualifications, to help identify the most qualified candidates.
- Predictive modelling: AI can be used to develop predictive models that estimate the likelihood of an applicant’s success in a particular program. By analyzing data from past applicants, AI systems can identify factors that are correlated with success in a program and use this information to predict the probability of success for individual applicants.
In conclusion, automating the process of shortlisting candidates for admission can be a valuable tool for institutions, helping them to more efficiently and effectively review and evaluate a large number of applications. Technologies such as application management systems (AMS), data analytics tools, artificial intelligence (AI) systems, and machine learning algorithms can be used to automate and streamline various aspects of the admissions process, including the collection and organization of application materials, the evaluation of applicants based on specific criteria, and the prediction of success for individual applicants.
Automation can also help reduce the potential for human biases or subjectivity to influence admissions decisions, ensuring that admissions decisions are based on objective criteria rather than subjective opinions or prejudices. Ultimately, automation can help universities more effectively identify and shortlist the most qualified candidates for further consideration.