7 Applications of Data Science in Education

Introduction

 

Data is such an asset that can be used by people to accomplish many feats. With the advancement of technology, the availability of data is also increasing and data science has been successful in analyzing, managing, and tackling the data every day. 

 

Due to this, many sectors have readily incorporated data science under their wings. It has revolutionized all the industries and helped them increase their performance and efficiency. There are many applications of data science in different fields and one of them is the field of education. 

 

Education plays a vital role in the upliftment of society and it is very important to have a strong and developed educational system. There is a huge amount of data coming from the educational sector. K-12 school and district records, digital archives of instructional materials and gradebooks, and student answers on course surveys are all examples of educational data. 

 

Data science of actual classroom interaction is also becoming more popular and common - it may be used to capture how classroom management and education are carried out.

 

Educational data is becoming increasingly valuable in higher education, as a growing number of online courses are being used. It even extends to the private sector, where personnel are educated and task issues are solved via online forums, threads, and distributed problem-solving methods through assignments. 

 

According to this source, here are many advantages that we will get on using data science in education, like:

 

• Educational data science would prepare teachers to investigate various types of educational data, as well as to give meaning to educational systems, their issues, and prospective remedies, and to build a deeper knowledge and experimentally verified forms of answers.

• Educators would be able to undertake data visualisation, data reduction and description, and prediction tasks with the help of educational data science.

• For practitioners, data visualisation may make information more intuitive and consumable.

• Many complicated records and fields of data about pupils can be deciphered via data reduction.

 

In this article, we are going to discuss the applications of Data Science in the field of education and how it helps in the betterment of the sector.

 

(Must read: Data science in risk management)

 

 

Applications of Data Science in Education:

 

1. Student Assessment Data:

 

In a classroom, there are many different types of students who are taught at once by a single teacher. It is very common for a percentage of students to excel and for a number of students to not understand the class properly. 

 

Assessment data can help the teachers determine their students’ understanding and modify their teaching strategies for the future. 

 

Earlier, the assessment techniques were not in real-time, but as Big Data analytics advances, it became possible for the teachers to understand the requirements of their students in real-time through the performance of their students. 

 

There are tools like ZipGrade that help in quicker assessments through multiple choice questions as it provides summaries and insights. This process, although beneficial, can be a little tedious and time-consuming.

 

 

2. Social skills:

 

Social skills are very important for any student as it plays a huge role in their academic as well work life. Without social or emotional skills, a student cannot connect or interact with his/her fellow peers and hence, fail to develop a relationship with his surroundings. 

 

Educational institutions have a critical role to play in supporting the development of social-emotional skills. This is an example of a non-academic talent that has a significant impact on pupils' learning skills. 

 

Even though there have been statistical surveys that can assess these skills, with the advancement of technology, now there are Data science techniques that can help in the better assessment. It is feasible to acquire such huge amounts of data and integrate it with current technologies to create better results using formalised knowledge discovery models in Data Science and Data Mining approaches. 

 

Furthermore, data scientists can use the collected data to use various predictive analytical tools to assist teachers in understanding the students' motivation for studying the course.

 

 

3. Data of Guardian:

 

Guardians/Parents also play an essential role in the education of children. Many troubled students perform below average in the school due to negligence of parents. So, it becomes very important for the teachers to communicate with the parents/guardians of all the students by arranging regular parent-teacher meetings. 

 

To ensure maximum attendance in those conferences, data science can be used. It is used to filter out the students whose parents did not show up and analyse the history or similarity between all the families with such behaviour. This can help the teachers personally communicate with those parents instead of sending generic emails or messages to all the parents continuously. 

 

(Must read: Applications of IoT in education)

 

 

4. Curricular data:

 

With the amount of competition increasing in the field of education, schools and  universities need to stay up to date with industry expectations in order to deliver relevant and improved courses to their students. 

 

Keeping up with the expansion of industry has become a huge problem for colleges, and hence they are adopting Data Science tools to evaluate market trends in order to accommodate this. 

 

Data science may be beneficial for studying industrial patterns and assisting course makers in imbibing relevant subjects by using various statistical measurements and monitoring approaches. Furthermore, colleges may use predictive analytics to evaluate demand for new skill sets and tailor courses to meet those needs.

 

 

5. Behavioral data:

 

There are many cases of misbehavior or indiscipline in educational institutions by students. Every time something like that happens, a designated staff member is required to put an entry in the system. 

 

The course of action for every incident can be determined by judging the severity of the action, as every action should have separate punishments. This can be a time-consuming task for the staff as they have to go through all the logs and then determine the severity to avoid unfair punishment. This is where natural language processing may assist. (Learn more through this NLP tutorial)

 

There should be plenty of log entries to utilise to create a severity level classifier inside a school that has been around for a few years. If the disciplinary staff and instructors could see it as well, it would save them time as the entire process will become automated. 

 

 

6. Instructor performance:

 

Students' grades are determined by their teachers. While numerous evaluation approaches have been employed to evaluate teacher effectiveness, the majority of them have been manual in nature. 

 

Student evaluations of instructors' performance, for example, have long been the ultimate standard for measuring instructional techniques. All of these approaches, however, are inefficient and time-consuming to evaluate. 

 

Reading student feedback and coming up with an analogy is also a time-consuming process. It is now feasible to track teacher performance because of a breakthrough in data science. This is true not only for historical data, but also for real-time data. 

 

As a consequence of the real-time monitoring of teachers, thorough data gathering and analysis are feasible. With big data technologies, we can also store and handle unstructured data such as student reviews. It is also feasible to evaluate the feelings of the reviews using Natural Language Processing and offer a thorough study of instructor performance.

 

(Related blog: 5 Steps of data analysis)

 

 

7. Student Demographics:

 

There are usually a lot of students attending a particular educational institute if it has been established for a while. There is a lot of data and demographics related to students like attendance, performance, extra-curriculum or even dropout rates that needs to be recorded by the institution. 

 

It is impossible for the teachers or staff to keep track of all the data personally, so they can take the help of Data science. Data about students may be found in systems like PowerSchool, ATS, from teachers, and in school network-only data pools. 

 

Using this data, the teachers can recognise the students who are performing poorly and solve any issues they might be facing. 

 

Conclusion

 

Data science in education is very essential as it ultimately promises a better future for society. Many modern educational platforms offer mixed autonomy, which presents an intriguing challenge in terms of data science approach. 

 

(Suggested blog: Benefits of AR in education)

 

Despite the fact that the learner has a lot of freedom in how she goes through the course, the teaching system has a lot of power to make recommendations and steer the student's learning path.