Question

Austin Peay State, a university near Nashville, Tennessee, is applying a data-mining approach to higher education. Before students register for classes, a robot looks at their profiles and transcripts and recommends courses in which they are likely to be successful or have higher chances of success. The software takes an approach similar to the ones Netflix, eHarmony, and Amazon use to make their recommendations. It compares a student’s transcripts with those of past students who had similar grades and SAT scores. When a student logs in, the program offers 10 “Course Suggestions for You.” This recommendation is based on the student’s major and other information related to that student. The goal is to steer students toward courses in which they will make better grades. According to Tristan Denley, a former programmer turned math professor turned provost, students who follow the recommendations do substantially better. In the fall of 2011, 45 percent of the classes that students were taking had been on their top 10 recommendations list. This data-mining concept is catching on. Three other Tennessee colleges now use Denley’s software. Institutions outside the state are developing their own versions of the idea.

1. What are the benefits and drawbacks of this approach to course recommendations?
2. Are there any data that should or should not be included in data mining for this purpose? Why or why not?

Answer 1

What are the benefits and drawbacks of this approach to course recommendations?

ANS:

Advantages:

1)To increase the success rate of students the early forecast technique will help the management to counsel the poor students at right time.

2) The different aspects like individual, social and psychological will be useful to measure student academic performance. This may lead to discover the students who are in risk and it help the management to take timely action.

3)Data Mining methods from educational data can be used to enhance decision making in terms of identifying students at risk, decreasing student dropout rate, increasing student’s success and increasing student’s learning outcome
4) we can understand likes and dislikes of student based on their data,Education Data Mining in data mining retrieve hidden knowledge by applying various techniques of data mining like clustering, rule mining, web based mining, test mining, neural network,Bayesian network, and many others,humans generally ignore these factors

Disadvantages:

1) Mind of all students are not same,even a small error on the suggestion can cause large impact on student's life.
2) Till date no machine is 100% accurate, this makes difficult to rely completely on such mechanism.

3) Privacy Issues

The concerns about the personal privacy have been increasing enormously recently especially when the internet is booming with social networks, e-commerce, forums, blogs…. Because of privacy issues, people are afraid of their personal information is collected and used in an unethical way that potentially causing them a lot of troubles. Businesses collect information about their customers in many ways for understanding their purchasing behaviors trends.

4)Security issues

Security is a big issue. Businesses own information about their employees and customers including social security number, birthday, payroll and etc. However how properly this information is taken care is still in questions.Many times attacker/ hackers stole this data for their unethical use.

5) Misuse of information/inaccurate information

Many companies sold data of consumers to third party for better and smooth functioning  of thier systems, and it is very difficult to know weather our data is in safe hand or not.