Data is another word for information. One of the biggest sources of data is our phones. The amount of data a single smartphone produces is massive.
When you combine all the data generated from all the smartphones in the world, it comes down to a number that the human mind cannot process. This is what the term big data comprises—enormous amounts of data from numerous amounts of sources.
Big Data in Education
The root of big data is making its way into education. Student information system or SIS is an example of this. This includes items such as the academic backgrounds of a student, their status, performance, demographics, and more.
Big data and education combine to form other applications and systems as well, such as LMS or learning management systems. Examples of LMS include Blackboard, Moodle, Canvas, and more. They provide information about student behavior. This information is complicated to obtain using face-to-face interactions.
These systems provide contributors of the healthcare sectors with critical timestamp logs. These can be used as data points. It is important to know that big data has three different levels. The first level is the microlevel, which is quick stream data. This consists of interactions of individuals in the learning environment.
Educators and IT leaders can obtain information from the quick stream interaction that students generate. This data can be instrumental in identifying the learning curves within students and the tendencies which can help elevate their learning. Micro-level applications include simulations, MOOCs, intelligent tutoring programs, and games.
Mesolevel Big Data
Another word for this type of big data in education is text data. This is what analysts use to look at a student’s writing in a digital setting. This was not possible before, but data scientists can perform in-depth analytics with the latest integrations of discussion forms, online assignments, and social media interactions.
Macrolevel Big Data
This type of data exists at an institutional level. This involves people learning about a learner’s demographics, class schedules, admission data, enrollments, grading, and more. This data is available at a macro level. There might be overlapping among the three above-mentioned data.
Microlevel Big Data
This data gives you insights regarding learner actions. In other words, it allows analysts access to information about how students interact. Nonetheless, microlevel big data can be generated using a small student sample. If a single student can generate thousands of data points, it pertains to all students' online activities. All of their interactions are tabulated and recorded to microlevel big data.
Impacts of Big Data in Education
Big data technology can contribute vastly to the education sector. As you know, there is a chunk of heterogeneous data that you can collect from various sources. Making use of this data-driven environment with the help of big data technology can revolutionize the education system.
Data collection from miscellaneous data sources can pave the way for superior student analysis and assessment. As mentioned earlier, a student’s performance is one of the most critical parameters in the educational sector.
Evaluating, analyzing, and guiding the student with respect to performance is an important yet painstaking task. Measuring parameters can be very tedious for teachers and education leaders. Collecting performance data and information of a student throughout their educational journey is critical for the student moving forward. It is also important for the faculty members who are guiding the student.
Data Mining Strategy and Learning Analytics
Learning analytics is a strategy where you can make use of different analytical techniques. At the end of the analysis, experts will move forward and create a report. Based on that report, you can analyze a student's performance. Efficient errorless reports automated by big data technologies will help faculty members of an institute to counsel students at different phases of their academic lifecycles.
Implementation in an Institute
Designing and creating a data mining framework in an educational system will involve a proposed framework. This framework will involve a system that provides flawless interactive service to all students. These services include the course information, and at the same point in time, involve course materials and the academic progress of the specific student.
If you have an enterprise system that makes collection and analysis of student data seamless, data mining and learning analytic techniques become easily possible. This system will fulfill the ultimate goal of an educational institute which is to council the particular student to achieve their career goals.
Other critical big data use cases in education revolve around tracking down student performance patterns. Apart from imparting quality education, faculty members need to have close contact with the student and be knowledgeable of their academic performance.
Big data technology’s capability of collecting and studying data has a big impact on the conventional learning process. It also allows educational institutes to counsel their students much better since students can provide their educational input parameters.
The heart of the big data process involves formulating the decision tree. Algorithms such as ID3 and C4.5 are efficient in generating the decision tree. Based on the algorithms, a decision tree can help institutes analyze data and pick a strategic decision based on the data that has already been collected. This will help faculty members guide students in an organized manner.
K-Means and Hierarchal Clustering are also two algorithms that educational institutes can use for data analysis as well as incorporate AI-based machine learning into their enterprise system. This will drastically improve enterprise workflow.
To simplify the implementation process of big data in the education sector, it starts with collecting data from various sources, processing information from the data, applying the data in the data mining process, and exploring results extracted from the particular data.