Big Data is big in nature, however Education data is not that big yet compared to Big Data. Quantitative analysis, Audio-Video recorded data of Education related research and general research work conducted by the academic institutions is steadily increasing. The data change is started within our education system as our students are taking exams, courses, conducting their research using computerized means. This means that the searching behaviors of students or whoever associated with education can be collected and trends can be drawn for the fields, which are under study by certain set of students to draw a trend means to predict, what might lay ahead for these students and where these students can get their research related material or help. This also means that these sets of data are now scaling and can be utilized for any such analytic needs.
Education Data Mining can be extremely helpful in doing inferences, predictions and more to establish students behavior and attitude as well as concentration to his/her education. The results found in this sort of analytics can help better the educational system as all stakeholders can look into the trends found once analytic reasoning is applied on the data of student related parameters. Usually we use regression techniques to analyze data, When we unitize the data into statistical numbers for analytic reasons, usually the produced results can be plotted on a graphs and trends can be found in terms of lines or combination of several data points as a concentration of some student behavior to learning or researching or any such related activity.
Classification is another method that we can use to analyze data, no matter this data is of researchers in a lab, or students in a university department or a product launched for consumers or a service provided to organizations in terms of B2B e-Commerce or supply chain needs. Let us take an example of a service analytic reporting to related stakeholders. Service utilization by general consumers, survey filled by the (many/few) consumers about, how they felt about the service, internal stakeholders' review of service, testing done on the service by quality assurance stakeholders can be considered as few parameters to be converted into labels. We can also consider a variable, whether the provided service is good or bad or there are other opinions, which can be taken into consideration by the designers for the release of next version of service to improve results.
We can use several algorithms for analytic needs, few can be, such as "Step Regression, Logistic Regression, Decision Trees or some sort of Classifier Algorithm". If we look into Step Regression, gives us the results in the terms of 0 or 1, if we look into the given service analysis example, this means, whether consumers used service or did not use service at all. These techniques are equally applicable to the field of Education to find out the ratio or some other trend, how students are performing of a certain school/district/college/university of one state or a country or can be expanded to the available Big Data for a certain area for research purposes.