Use of Datasets by the EdTech Sector

While the education system has slowly evolved with time. The pandemic coupled with the convenience of remote learning has given a major boost to the EdTech sector. E-learning or special courses are no more limited to the professionals who wish to upskill for their jobs. It has found a new customer base in school children. The usage of EdTech for learning their school subjects and solving test papers. EdTech companies are helping students in multiple ways. Let us take a look at what type of datasets the education sector uses.  

1. Companies can update one set of resources, be it videos, or textual information, or question papers. They have thousands of people take advantage of it.
2. E-learning often allows individuals from across the world. They come together to take part in various learning and team building activities. 
3. It is easier to keep track of the progress of individual students and offer special one-to-one doubt clearing classes. Separate sessions are also done for a few if required. 
4. You can study from anywhere, at any time, as long as you have a stable internet connection (a lot of these websites also allow you to download and save content for future consumption).
5. Companies are using ready to use datasets to train students on new-age technologies like Data Science and Machine Learning.

But the EdTech sector needs a lot of data to keep their content updated and to keep track of the latest trends and topics to create new content on them. A  lot of this data is scraped from the web, some manually, and the rest through code.

Data Requirements of The EdTech Industry

The EdTech sector needs a lot of data to keep churning new content. They can come up with interactive sessions or well-explained videos, only once they have their core content ready. For this, web scraping is one of the best options thanks to its massive reach.

Let’s discuss some data sets that EdTech companies need to gather to create better content.

• Question papers of previous years for specific examinations: While a lot of these may not be available in the best formats for earlier years, most of them available online today. Having a dataset of these questions would help in creating content that can help students in getting familiar with the exam paper and ultimately, score more marks in these examinations.
• Content related to each topic: Let’s say there are 10 topics in a subject-paper, and a company needs to create fresh content on all these topics. The content should be easy to understand and help students to grasp every concept. This is possible only if a dataset of existing content is present, and the educators can find out what is missing in those or what can be added to those, or how they can be explained more easily.
• Syllabus and exam patterns: Companies need to have updated datasets for syllabus and exam patterns for different exams. This helps in multiple ways. On one hand, these EdTech platforms can guide students in preparing for their exams, and on the other, they can use the information to create content on the most common or important topics and subjects first. 

• Important concepts on each topic: Every subject and topic has some important concepts which form the base of one’s understanding. A dataset of these helps companies in deciding what to create content on first or to decide which topics to stress on most. 
• Use of datasets in training students: For training students on topics like ML and Data Science, pre-validated and pre-tagged data sets are very useful. These data sets can be used to train and test models and also help students in gaining hands-on experience so that they can go on to work on real-life problems.

All these datasets directly affect and alter the course of businesses and their availability can help in deciding quarterly or yearly goals. These are the basic data sets that form the core of these companies and without these, companies would fall behind in the race and also lose the trust of the students who join these new-age online platforms with certain expectations.

Use of Alternate and Non-Conventional Data by EdTech

Like all other industries, the EdTech sector has also begun its quest for integrating alternate data into business processes. Let’s take an example. In the job industry, certain keywords have seen a boom. A few examples are- data-science, digital-marketing, and DevOps. Most of these keywords were non-existent a decade back. EdTech companies have kept up with these trends by offering courses on these specializations. They have onboarded the top professors from the best colleges. Along with industry experts, to offer students an opportunity to undertake these new-age courses to remain relevant in the job market. 

Content also needs creating in a certain format to make it most helpful for consumers. For example, video content is most preferred by students for learning. This sort of information can be realized by creating surveys. Or scraping data from previous research studies that have been conducted. 

Now let’s talk about non-conventional data sources. Almost everyone scrapes textual content from the web, to aggregate the data, and in turn, create new content. However, extracting data from podcasts (audio content) or videos may be more difficult, and few may be doing it. But getting a dataset of these content may prove to be much more useful for companies. Then getting simple textual content. 

Use of Datasets for Training

EdTech companies and even colleges are training students on data science and machine learning topics. These courses require a lot of data to create and validate models and learn how to work with data in general. Most developers using Python for data manipulation and data cleaning use packages like Pandas and Numpy to work on the data and can hone their skills using large datasets. 

You can validate both the speed and the accuracy of your code using ready to use datasets. When designing new algorithms that are supposed to be better than the older ones. It is always essential to test your algorithm on multiple data sets to validate your conclusions with utmost certainty. 

Once the training sessions are complete, students can also take on a single dataset as a case study. They then complete a full data processing workflow on it- from sorting the data to extracting insights and creating meaningful visualizations. 

How can these Datasets be Acquired?

Any of the datasets that we discussed above created by scraping content off the web. But scraping content off the web, will not directly result in a usable dataset. Multiple steps followed before you have a dataset that can be used by EdTech players. This involves finalizing data sources (websites), conducting a feasibility study for scraping these websites, scraping the data, cleaning the data, sorting and labeling the data. Finally converting the data into a specific format and storing it in containers or databases. This is done so that the business teams can use it. 

The processes above are not just complicated but will also vary for different websites. Processing data from one website might be straightforward, while another may need manual intervention, scraping data from one may be simple, while another may be challenging. All this adds up to a major problem statement that can be solved in one of three ways-

• Subscribing to a no-code software-based web scraping tool (which comes with certain limitations).
• Having your team build a scraping module that the business can use (using languages like Python or Golang).
• Providing your requirements to a DaaS provider like PromptCloud that offers custom data scraping solutions or use its datastore called DataStock, which provides ready to use datasets from various sectors that can be used on the go.

The Role of DataStock

Our offering, DataStock, is an online store for datasets where you can buy structured data from different sectors. Datasets range from recruitment, healthcare, and travel. The data comes in a clean and ready-to-use format that can directly be used for building machine learning models. And other recommended systems, or for spotting trends via market research. Pricing starts as low as $20 and there are multiple free datasets as well. You can also download a  sample dataset. You can do this to check the data points and see how well it fits your use case. 

EdTech companies can use some of these datasets to perform market research or find trending requirements. Be it in the job market or for training in ML or Data Science courses. In case they are unable to find a dataset and have a different web scraping requirement. They can always head to our main website to submit their requirements. You can then sit back and get a custom solution that fits their bill perfectly.

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