Improving educational big data based on web mining and association rules

The term "big data" refers to a variety of data types, including text, images, videos, graphics, and more. With a Graphics Processing Unit (GPU)-based framework, the training time for such a model may be reduced from many weeks to only a few hours. Numerous activities and a growth in information processing have resulted in a data overflow. Unstructured data comprises 80% of all organized data. Only completely unstructured, relevant sources should be examined and used. An essential aspect of web data mining is determining which pages are accessed together during a single network visit using association rules. The purpose of Education Data Mining (EDM) is to provide tools for the analysis of particular categories of data generated in a learning setting. In online usage mining, association criteria are used to identify web sites that are linked together via associations rather than via direct connections. Apache Spark is a framework for data processing that uses several computers to distribute processing over very large datasets. Much of the difficult work needed for large data processing and distributed computing is abstracted away by Spark’s API. The web scraper takes the required links from the web, extracts the information from the source links, and stores the information in a csv file. Web scraping is not illegal, but it might not be ethical. Finding trends in teachers’ and students’ system usage is one of the key goals of educational data mining. One of the numerous characteristics of Educational Big Data (EBD), which is generated from the activities of many objects in various learning situations, is complexity. Our research aims to develop and implement a hybrid model based on web mining and recommendation systems for educational big data applications. We’ll start by discussing web data mining approaches (K-means and association rules, for example) and selecting one or more techniques for recommendation systems. To complete the services provided by that educational application, one of the environments or platforms for handling big data is selected.