One such example is Google. To be successful, Google collects and analyses data from all of its users to make the website, ads, search results, and other features optimal for each person using their services. Similarly, Universities are now collecting information on its students but that raises questions. Does the university use that information? How does the university use that data that is in ethical manner for the student and the university? The article “Big Data on Campus” by Marc Parry explores this idea by bringing up the data collecting, and resulting actions to other data collecting businesses and services. Amazon, E-Harmony, and Netflix are just a few of the data collecting companies that article compares to Universities. One of the examples that struck a chord was the collection cad swiping data. With that data, Arizona State University or ASU can track which buildings the student is in, how long they stay, where they eat, and other important data on a student’s daily life. Hypothetically speaking, this data can be used to “infer social ties” between students however, that information can be used by school administrators or hackers to student life patterns. This can be dangerous because the administrators may be able to track the leaders of student protests while hackers with predatory history could track his next victim. While these are very extreme and negative ways this information can be used, ASU is using similar processes to track student successes in classes to help the student choose classes in which the student would succeed the most in. This is a more practical use of the data in my eyes however, it does raise the question, by using this data to help the student choose classes, is it really helping the student? The article relates this use of the data to Netflix and it’s suggested. If you own a Netflix account, you know that many times the suggested items on Netflix are not things you would normally watch despite the loose correlation to the things you do like to watch. In comparison, by using this data for students and their classes, universities may suggest classes that may follow a certain pattern or algorithm, it may not be the best choice for the student. They say if you witness a crime, and do not say or do anything about it, you are an accomplice in the crime. A similar concept is on the minds of those who are collecting big data for Universities. In the article “Ethics, Big Data, and Analytics: A Model for Application”, the writers explore the many questions that arise with the collection of this data. Using a similar situation to ones previously mentioned, data collection in Perdue University can signal a student when they are falling behind in academic performance. Each signal comes with feedback from the instructor on how to improve the students’ performance. Over time, the university saw progress and improvement in students while the signal program was in place compared to the time before the program. This shows us that data collection in universities can be positive but there is a flip side to this. In the previous article, data collectors were able to accurately predict a student’s grade by the
One such example is Google. To be successful, Google collects and analyses data from all of its users to make the website, ads, search results, and other features optimal for each person using their services. Similarly, Universities are now collecting information on its students but that raises questions. Does the university use that information? How does the university use that data that is in ethical manner for the student and the university? The article “Big Data on Campus” by Marc Parry explores this idea by bringing up the data collecting, and resulting actions to other data collecting businesses and services. Amazon, E-Harmony, and Netflix are just a few of the data collecting companies that article compares to Universities. One of the examples that struck a chord was the collection cad swiping data. With that data, Arizona State University or ASU can track which buildings the student is in, how long they stay, where they eat, and other important data on a student’s daily life. Hypothetically speaking, this data can be used to “infer social ties” between students however, that information can be used by school administrators or hackers to student life patterns. This can be dangerous because the administrators may be able to track the leaders of student protests while hackers with predatory history could track his next victim. While these are very extreme and negative ways this information can be used, ASU is using similar processes to track student successes in classes to help the student choose classes in which the student would succeed the most in. This is a more practical use of the data in my eyes however, it does raise the question, by using this data to help the student choose classes, is it really helping the student? The article relates this use of the data to Netflix and it’s suggested. If you own a Netflix account, you know that many times the suggested items on Netflix are not things you would normally watch despite the loose correlation to the things you do like to watch. In comparison, by using this data for students and their classes, universities may suggest classes that may follow a certain pattern or algorithm, it may not be the best choice for the student. They say if you witness a crime, and do not say or do anything about it, you are an accomplice in the crime. A similar concept is on the minds of those who are collecting big data for Universities. In the article “Ethics, Big Data, and Analytics: A Model for Application”, the writers explore the many questions that arise with the collection of this data. Using a similar situation to ones previously mentioned, data collection in Perdue University can signal a student when they are falling behind in academic performance. Each signal comes with feedback from the instructor on how to improve the students’ performance. Over time, the university saw progress and improvement in students while the signal program was in place compared to the time before the program. This shows us that data collection in universities can be positive but there is a flip side to this. In the previous article, data collectors were able to accurately predict a student’s grade by the