Khalid Nawaz

  Application of Big Data Techniques A high dropout rate — 28 percent of freshmen in one program — is a major obstacle in online STEM education. Teachers collected a huge amount of data to attempt to tackle this, including assignments submitted, posts made in forums, marks on quizzes, views of video lessons and actions in LMS logins. After detecting stress and disengagement indices from chat contributions via natural language processing (NLP), regression models were used to predict who among the students would be at risk of performance dropout. Cohort dropout risk heatmaps, weekly teacher trend reports, and individual student engagement dashboards were applied to visualize this data. These findings enabled more targeted interventions: teachers were automatically alerted to reach out individually to students showing early warning signs, those at risk received specific prompts (e.g., offers of tutoring, inspirational messages, or resources); and teachers were given individual feedbac...

     Implications for Individuals and Society You need to make sure to consider the broader implications for people and society as educational institutions are using predictive analytics to advance learning outcomes. The insightful results derived from data have had a positive impact on student support and have allowed for interventions to happen in real-time, thus, problems get mitigated early on, but at the same time, they bear negatives. Local realities that exert a significant impact on a student's performance such as part-time jobs, family obligations, or health problems are sometimes outside the scope of the predictive models used. Students have expressed personal fears of academic surveillance and that the machines are watching them and are responsible for grading. Many students requested to be informed about the method these predictions were based on and the transparency and justice of risk scores were questioned. It is the rise of the need for explainable AI in e...

  Applications in Business, Science, and Society   Big data is surely a principal factor which better performance is generated in the business world, science, and society rather than just the educational field.It is the data that universities and colleges are analyzing and using these days, disconnect with the absence of any kind of innovations to get the best results from the data misuse. Despite several different problems of the students' withdrawal such as case refundation or ineligibility for funding, the improvement of these outcomes can be achieved. Of course, many educational institutions have introduced big data which enabled firsthand new ways of conducting research. One particular approach in this texture, which is The Interaction Frequency of Students with Content, that one might implement,was thoroughly researched by Gunawardena and Zittle [...]This field's application is brought by this learning analytics and digital pedagogy research, which represent the future ...

  Explaining Big Data Concepts Massive amounts of data are coming out of educational platforms because of the rapid growth of online learning, everything from discussion forum activity and video watch time to the frequency of login and assignments submission available. The titanic volume of data, often called “big data,” is being utilized by institutions for the purpose of the improvement of learning results and for the thorough understanding of student behaviour. Edutrack is one such example, where the amount of data annually went up from 1TB to over 10TB, making it necessary to use smarter data strategies instead of manual ones. This comprises, among others, the situation when manual progress tracking was the main method of monitoring, and the end-of-semester survey was the instrument that gave the teachers clues about the best topics for the next semester. The effective scope of the latter was not only limited by its low response rates but also by the time taken to supply an inp...

Anomaly Detection Problems Anomaly detection is the process of recognizing an item which follows an unexpected pattern. This problem is significantly important because it allows the detection of errors, dangers, or even illegal acts in large arrays of data. Big data tools can quickly scan through thousands or millions of records to find anything that looks unusual. For instance, banking is a sector where big data can be utilized to detect fraud. If a card is utilized in a different country without your knowledge while you are still at home, the system considers it to be “anomalous” behavior. It informs the bank of the situation and hence, your card may be blocked to stop the misuse. The reason for such a quick response is that big data automatically adapts to your changes in behavior. Big data are monitoring tool of machines in factories. They pick up information such as a vibration, a temperature, and a speed. If a change occurs suddenly, the system gives the staff a warning before th...

  Prediction Problems Prediction problems are where big data is used to make the best guess about the future. This is accomplished by analyzing past data in order to identify trends or recurring patterns. If we know how something behaved, we can often be able to predict how it will act next. The first example that comes to mind is weather forecasting. Weather stations collect huge amounts of data every day, recording temperature, wind, humidity, pressure, and many other parameters. Big data systems look into years of such data to guess if it will be rainy tomorrow or if there is going to be a hot spell. Moreover, these forecasts become the basis for a person’s daily routine and safety. Also, businesses make use of big data to decide on the number of products that they could probably sell next month. This allows them to plan production, storage, and transportation accordingly. Supermarkets might use big data to guess how many people will buy cold drinks during a hot week. This help...

  Pattern Recognition Problems Pattern recognition is the utilization of large data in its most common form. It basically is the identification of similar behaviors or trends in a large amount of data. The tools designed to work with big data can skim through millions of records to identify human repetitious activities. Online shopping is a perfect example of this. E-commerce giants like Amazon utilize big data to monitor users' browsing history, adding items to the cart, and completed purchases. So, if lots of people buying a phone also buy a charger, the system learns this pattern. After that, it suggests chargers to other phone buyers. This is called a “recommendation system.” Besides, pattern recognition can also be applied in such areas like video streaming platforms. Netflix and YouTube get the information about what kind of videos people watch most and what they usually watch next. Based on that, they can propose you some new videos. All of this is done since big data identi...