Why Educational Systems Should Consider Big Data

Advances in technology have enabled good decision making in most educational institutions following the increased use of big data. The policy makers in the institutions have been using big data to understand the sentiments about the school and make systematic improvements to the student’s performances. The term big data refers to a large amount of information flowing through various channels that only use computers for analysis. Learning institutions generate an immense amount of student’s information that would be hard to capture and manage through conventional means. Therefore, big data comes in handy in helping improve the processing of data and increasing the storage capacity of the institutional data.

Understanding Big Data

Students’ performances and experiences such as eating, social life, study, and sleeping have a high effect on their academic performance. Negative or traumatic experiences have a direct impact on the student’s retention abilities. Therefore, most institutions now use big data to look into various aspects affecting the performance of a student. Academic institutions collect large quantities of data, but the problem lies in the analysis making it harder for the analytics to make data-based decisions and improve the organizational effectiveness.

Why Big Data and Not Small Data

Academic institutions collect data for many years, meaning that compiling the materials and analyzing them may produce a wealth of useful information and allow better prediction of future outcomes. The educational content is also broad meaning that big data analysis is bound to generate concrete information. This means that analysis of the high amount of data is highly likely to give clear and tangible information about an academic institution compared to the small data. Classrooms have been using learning software and digital games, which generate data that the school administration can use to get information on student learning.

Complex big data allows the prediction and analysis of behavior based on the demographics, thinking processes, educational intentions, environmental factors, personal background, and genetic predispositions. They then use the information to predict in the students that have a high likelihood of dropping out of school or becoming disengaged and counter the issue before it happens. Below are some of the ways that big data may affect the education institutions:

1) Formulation of the personalized content

Analysis of a student’s personal information, genetic composition, and their life experiences among others helps in understanding their performance levels and capabilities. The teachers can then formulate the studying materials depending on the results of the big data analysis.

2) Evaluation of the Students Performance

There is an increase in the number of students studying online making the collection of data and evaluation of the performance easier. Dashboarding has also allowed quick accessibility of real-time and accurate data. This has made it easy for the researchers to track the student’s performance and increase the effectiveness of the formulation of personalized content and directing assistance in the right places. Some platforms such as Hadoop Tableau have the ability to collect and store a high amount of real-time data allowing coverage of a large number of people.

3) Effectiveness analysis

Big data helps the teachers in learning what worked or failed and understand the patterns that would have contributed to the outcomes. The assessment of the learning tools and techniques assists teachers in the development of interactive learning materials that would make education more fun.

Summary

Therefore, every learning institution should consider using big data to improve the student’s performance levels and the effectiveness of their teachers. The ability of the schools to monitor the student’s performance in the job market helps improve on how they equip the students in their entry to the career world. Predictive data has also assisted in the decision-making and reduction of the number of dropouts in schools.