The goal of the educational system is to support student success. Yet, this aim is not that easy to achieve. Success is a complex matter that consists of and is influenced by a variety of factors. The approach to this issue differs across institutions. However, all educators agree on the fact that there is a need to improve the procedures of data sharing, processing, and collaboration among different departments. More and more institutions start to apply big data to predict student outcomes and, as a result, offer everything they need to succeed.
The Value of Predictive Analytics
Just like a marketing specialist determines a strategy to promote a product based on customers’ interests and buying habits, predictive analytics can help educational institutions to design an enhanced curriculum and support student success. It applies statistical methods to predict future events. In the education sector, it means getting insights and projections concerning student enrolment, use, and relevance of resources, student engagement, etc.
Predictive analytics has experienced rapid growth in education, especially in secondary and post-secondary institutions. Large sets of data, which concern different areas of student activity, help to inform decision making and development strategies of institutions. They support student success plans with reliable evidence. Data on class enrolment and test results, as well as demographic information, allow an institution to concentrate efforts. As a result, it serves to increase student retainment and supports success.
More and more institutions include data-empowered commitments to student success in their official statements. By utilizing predictive analytics, they get a clear focus for their further work and resource allocation. If you are a student and you struggle with a paper on predictive analytics, big data, or any other topics, asking a writer to “do my paper” is just what you need. The conceptualization of these terms may seem complicated because these trends are relatively new in the education sector.
The Role of Predictive Analytics and Big Data in Student Life
Big data provides meaningful insights to improve teaching methods and create new educational opportunities for students. Learners’ patterns of behavior and needs are valuable indicators for creating customized courses and a better learning environment. Naturally, both aspects have the potential to promote student success.
Big data technologies give educators the opportunity to see a big picture of student performance from the very beginning of their path. It includes interests, abilities, preferences, favorite subjects, attendance, enrollment in courses, extracurricular activities, grades, participation in group activities, etc. This list can be much longer as a trail of data that each student leaves behind is unique and diverse. These data sets can help educational institutions to improve their pedagogical practices in order to serve their students best.
In a data-driven curriculum, students can get individualized learning plans and enjoy a tailored learning experience. By using predictive analytics insights, teachers clearly see the strengths and weaknesses of their students. Therefore, they can provide learners with some extra attention and help them deal with problem areas.
Moreover, predictive analytics supports students with personalized career counseling. A comprehensive analysis of a student’s progress will help educators to predict the area where a person is likely to succeed. As a result, a student gets an industry to focus on and can concentrate efforts to reach his or her goals in this area.
However, the role of big data in student success is not limited to academic performance. It covers almost all areas of student life, including classroom climate, participation in community life, culture, extracurriculars, and employment. All these factors influence student success to some extent. For example, some students may experience financial difficulties when others may struggle because of communication issues. If an institution does not take measures to help these learners, the issues are likely to take a toll on their success.
Early detection of students who are at risk of underperforming allows the staff of an institution to develop and implement interventions to deal with problem areas. Reaching out to students and offering them a helping hand is crucial. It saves them from being isolated and gives students motivation to continue their studies.
Predictive analytics allows educators to build a supportive environment for student performance. This process should be based on the combination of institutional vision and goals with reliable predictive models. This way, teachers and administrators have everything they need to prepare timely interventions aimed to enhance student outcomes.
The use of predictive analytics and big data in higher education is still a new trend, even though it grows rapidly. Therefore, there are still no exhaustive recommendations on its application. The only universal rule is to empower the process with ethics and security measures.
This is a long journey that is yet to be completely explored. Nowadays, we have already witnessed the potential of predictive analytics in decision-making and learner support. It promises to be effective in driving student success with the right approach and willingness to make a change.