We know you are probably tired of hearing that data matters. And it does. It’s present everywhere. Universities are beginning to harness student data by developing student-centric accessible data to improve the quality assurance of the universities. Why? To maturate their program capabilities and empower higher education instructional designs. 

How is it done? Although, the process is a multi-hierarchical task, a few of which can be broken down in simple terms as collecting student mailing lists of enroll candidates, presenting credits, scores, courses selected by students, qualification of educators, assessing attendance lists, total enrollments in the program, etc. Thus, a proper data management system assists in diagnosing program shortages and serving personalized messages and support through college mailing lists. This data is used by universities to build effective curriculum as well as by prospective students to decide which program to enroll in. 

Having an estimate of the student database joining in year courses, university investments to run research, program and running proper administration can be efficiently planned to track school/college funds and resources. Let us see some of the best practices for using student data for student success. 

1. Adapting Predictive Analytics

Adaptive predictive analytics is one of the key practices higher education institutions use student data. Multitudes of higher education institutions are keen to measure the advances of student data analysis and ways to improve the quality of services they offer. Analyzing past student data helps predict existing students’ needs and target prospective students through recruiting and institutional aids. 

Predictive analytics tools allow vendors to facilitate the ethical use of data and ensure accurate integration to eradicate the chances of misidentifying students. The Algorithms based on which student data are collected must be transparent and tested for disparate impacts on student populations. Predicting analytics should be flexible adhering to all protocols to maintain student privacy and security. If necessary, employees must be coached to interpret data accurately and avoid implicit bias.

2. Protect against Implicit Bias in Algorithms

Many educators believe that predictive analytics models are not entirely free of implicit biases. These biases may result due to faulty algorithms created by human programmers’ decisions. As a result, algorithms may conceal the needs or accomplishments of a particular student group, at worst, extend the impacts of structural racism.

Universities must obtain neutral observers deployed to track these algorithms to eliminate present biases if any. It can be achieved through an internal analytics support team that is reliable enough to refine algorithms to ensure equal treatment and viable student access. 

3. Coaching Employees to Use Data effectively

Campuses are displaying a shift towards showcasing their mindset as student-ready. However, advisers, faculty, and support staff should receive coaching to use this data effectively. They must be taught to be hypersensitive to information such as students nearing financial aid limits; facing challenging life situations, food or housing insecurity; or academic issues. Additionally, administrators and advisers should be trained to facilitate opportunities for collaborative decision-making with students, rather than making decisions on their behalf.

Institutions hold the capacity to identify at-risk students before a session begins. These cases can be cross-checked depending on the student’s past experiences. Once identified, educators must be equipped to take appropriate measures to help students enroll in classes that would prepare the student for more difficult coursework.

4. Ensure student authority to waive data privacy rights 

It is critically important to let students enjoy a choice to opt-in or out of allowing his/her data to be used by institutions. They must be kept informed about how and where their personal information is used on the campus. Oftentimes, students may not want to disclose their personal information without their permission such as mental health concerns or location tracking of their visits on campus.

5. Selectivity over Student Data accessibility

Even on campuses that promote a holistic view of the student, not everyone needs access to all of a student’s data. Having data governance and custodial practices in place allows the right people to see the student data by the right people only. For instance- a faculty member might require piece of information of a student’s data such as scores in tests, attendance, or class participation. While a sports coach might notice the student’s behavior in the field, a music teacher can inform the student’s interest in other activities. 

When we talk about student data contributors, it is usually the Instructors, administrators, and students, who participate in the data production process. All of these participants should come to a common understanding of the basic purposes and limits of data collection. This can help to review a balanced proactive student identification in need of a sustainable and ethical student privacy approach.

6. Prepare Comprehensive Data Review Process

Student data is present as several pieces of information that must be amalgamated as one whole through all the contributors to review as a final assessment piece of the puzzle. This consented data represents what is called- a holistic view of student data. Therefore, there should be a collective decision-making committee that governs student data usage. 

For example, to support a student’s learning, institutions might need data acquired from teachers, support teams, administrators, and families. Moreover, the third party, such as student exchange programs, and academic meetups are also helpful in deriving student data

There may not be standard answers available as to who owns student data, who gets to see the student data or the ways to ensure that students also get a chance to make decisions related to their data. These issues are surfacing to the forefront and becoming parts of vital discussions to transform data technology for ensuring student success in the coming decade.

Schools/ colleges/ universities require a student database to put in best practices to identify the success rate of their programs and education system. “Amerilist” offers a comprehensive solution for targeting these students by developing most lead generating student mailing lists.