Exploring Uses for Data Mining in Academia

The origins of data mining can be traced back to the 1970s when computer scientists were making strides in Artificial Intelligence. Some of the first successful applications of data mining were for market research and to... [ view full abstract ]

The origins of data mining can be traced back to the 1970s when computer scientists were making strides in Artificial Intelligence. Some of the first successful applications of data mining were for market research and to detect credit card fraud. Similarly, data mining has been applied to numerous fields ranging from the financial sector to homeland security. In the past 10 years a growing number of universities have turned to data mining to better understand learning trends of students, to improve retention rates and to analyze learning outcomes associated with various accreditation agencies. Researchers have used data mining to predict risk factors that impact a student’s ability to successfully complete a course, to detect user patterns associated with passing grades, and to devise retention strategies for at risk groups. Platforms such as Web CT and Blackboard collect data regarding grades, assessment completion traits, frequency that course material is accessed and discussion board participation. The platforms seamlessly provide data analytics to help academics with understanding how their students are performing at various junctures of the course. Since many of the learning platforms already collect extensive data on each student enrolled in the course, there are limitless implications for how academic institutions can use the data, data analytics and data mining techniques to improve the overall learning environment. Typically, most data mining techniques can be classified in 4 basic categories: predictive models, descriptive models, pattern models and anomaly detection models. The purpose of this research is to explore different types of modelling techniques, review fundamental concepts of data mining, and examine how data mining is being used in academia.