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3 Ways Big Data Is Changing Education Forever
The recent rise of data analysis has changed the old education industry's mode of operation. Unfortunately, the education sector has been slow to adapt to the application of technology, so it has led to the rise of EdTech in its various forms, and it is currently driving the incorporation of advanced data technologies to fine-tune the educational process.
It is undeniable that big data is changing the way of education. First of all, with a personalized learning process, he can track the subject's learning activity history, integrate all aspects of his ability and behavior, and establish his own personal learning path for students. According to the learning experience of the subjects themselves, customize the course and length of the words you want to take. Through personalized design, students can effectively improve the effectiveness of learning, so that students have a better learning environment. Secondly, this technology helps to measure students' classroom performance, even in ungraded courses, it can monitor students' performance in all aspects. It can be observed from the data such as if a large proportion of students had to go over a material multiple times, making it more explanatory would likely help future students understand it better. Last but not least, in general, students’ interest in education under the formal rule has been declining year by year. Therefore, by analyzing big data and arranging courses and schedules according to their own preferences, students are increasingly attracted to conduct courses remotely. However, the application of technology may still bring some drawbacks, such as privacy and information security issues, users need to pay more attention to it, and the promoters must also propose appropriate solutions.
Big data in our daily life
In today's life, the application of big data is ubiquitous, and sometimes we are even used to it or even unaware of its impact on our lives. If there is no big data, the operation of society may be chaotic. Five applications are mentioned in the article. And I will choose three of them to discuss. First of all mobile maps and GPS. About ten years ago, road travel still had to rely on maps or acquaintances to lead the way to avoid getting lost in high mountains or other sparsely populated fields. Now, as long as you follow the built-in navigation on your mobile phone or the map on the dashboard of your car, you can carefully plan the itinerary for us. Nowadays, with the introduction of more mobile apps and more smartphones as well as the liberalization of telecom grids, the amount of Big Data that goes into providing accurate real-time directions is staggering. Secondly, the rise of online shopping has greatly changed the industry's operating model due to the application of big data in the retail industry. Retailers use big data from the moment you begin your search through targeted advertisements all the way to the delivery of your parcel, in Amazon’s case even placing the package inside your home with its new Amazon Key service. Moreover, big data is constantly being used in the context of smart cities to plan urban centers. The underground system can be implemented to track passenger flow. Through the data of the fogging system, the time when passengers enter and exit the station is recorded so that the operator can use the data to determine the peak time of the ride and adjust the usage in a specific area in a timely manner. Furthermore, the passengers can be informed of the delay time, so that they can adjust the itinerary in real-time.
Amazon: Using Big Data to understand customers
This article will explore how Amazon uses data to understand customers. Amazon has thrived by adopting an “everything under one roof” model. Amazon offers customers a variety of choices, but in the face of so many choices, customers often feel caught off guard. In order to solve such a situation, Amazon collects a large amount of data from customers and analyzes it to build a more complete product browsing engine. As long as Amazon knows more about the customer's needs, he can more accurately predict what the customer wants to buy. What's more, once the retailer knows what you might want, it can streamline the process of persuading you to buy it – for example, by recommending various products instead of making you search through the whole catalog. Amazon’s recommendation technology is based on collaborative filtering, this means that we can choose what we want by taking pictures, and at the same time provide products purchased by customers with similar orientations as suggestions. Amazon will collect any data provided by customers for analysis. In addition to the products that have been viewed or purchased, the company will also monitor the delivery address and how to comment on the product. Amazon collects data from users as they navigate the site, such as the time spent browsing each page. The retailer also makes use of external datasets, such as census data for gathering demographic details. Amazon provides too many choices and services to customers, making it difficult for customers to make purchase decisions and reducing their desire to use the website. Therefore, a well-recommended recommendation engine is very convenient for consumers. Significantly increase consumers' desire to buy, while creating more business opportunities.
How Instagram Uses AI and Big Data Technology?
Instagram was founded in 2010. In less than ten years, it has surpassed other social media competitors and even became the fifth most downloaded app by the end of 2019. Instagram is a sharing platform that can use photos and videos. It uses more than one billion users every month. It is already one of the most acclaimed and famous applications in the world. In the young ethnic group, the mobile phone program used almost every day, just like the line of this communication software. Continuously upgrade and update its functions in order to maintain the popularity of the charts. Millions of information flow on this platform every day. Through the use of users, big data analysis is formed to track user preferences and provide posts that attract their attention. And how does Instagram do it? First of all, users can grasp specific topic information or activities, events, restaurants, attractions, etc. through tag-laying. Basically, Instagram recognizes accounts that are more or less similar to one another by adopting a machine learning technique termed as “word embedding”. In order to make suggestions, the system will first observe the seed account, that is, filter the articles that the user previously liked or saved. After that, it will be screened to filter out some content that is misleading or violates the policy. The top 25 recommended content will be sent to the user's browsing interface. Moreover, through data extraction and analysis of customer preferences, advertising is more effectively targeted at interested groups to achieve greater revenue benefits. Last but not least, due to the rapid growth in the number of people using Instagram, the content, and services provided by the platform will also become crucial. To do this machine learning algorithm was put to work to go through all the content and carefully comprehend which of the content would be more relevant for its users, in order to design a personalized feed for each of them.
How big will the big data analytics market grow?
Since the application of big data analysis has flourished, more and more industries have demand in this regard. Zeng Chang's forecast report on big data in the Western Asia-Pacific market shows that big data is more prosperous in this region. The report says that big data revenue in the APAC revenue will get to $15.1 billion by 2022, and it also expects the largest businesses to account for almost half the market share during the forecast period. In addition, the Chinese market in the Asia-Pacific region will be its largest market, followed by Australia. These data indicate that the growth of the market is inextricably linked to the vast geographic location. At the same time, it can also help companies to play a bigger role in business perception, thereby promoting the market growth. However, many companies have encountered some difficulties in the process of analysis, especially in the case of the case data. Although these data are not often used in enterprise operations, they still hinder the acquisition of data. The company has realized that data defects may affect subsequent analysis and processing. So the company is preparing to recruit professionals to optimize their data. As such, the interest in data careers is another factor that’s boosting the market value. Some companies are already directly benefitting from the way more people want to pursue careers related to data. Many people use the Internet of Things in their lives today and collect a lot of data. This data analysis allows companies to better understand customer needs while helping to enhance market competitiveness and revenue.
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