Data mining applications in higher education pdf

In summary, association rule can be used for opening new colleges, offering new courses and specialisation based on certain rules. Clustering Clustering is grouping similar objects. Rajshree et al. According to Larose cluster does not classify, estimate or predict the value of target variables but segment the entire data into homogeneous subgroups. Heterogeneous population is classified into number of homogenous subgroups or clusters are referred as clustering Berry and Linoff, Furthermore, clustering task is an unsupervised classification.

For example, students can be targeted after segmenting heterogeneous students into similar groups. Furthermore, clustering task in education sector can be based on enrolments, transfer, readmission, course selections, specialisation, age, gender and behaviour of students. According to Chen et al. Ngai et al. Huang et al. Perceived usefulness and perceived ease of use are the factors that affect an individual intention to use data mining tools. Figure 1 shows the techniques of data mining. Neural Networks It is techniques which can be used for classification of large complex data.

It can be used to study course selection by students, student course satisfaction, and specialisation selection. The input data is also represents by neurons which are connected to the prototype neurons. Each such connection has a weight, which is learned adaptability during learning. Decisions Tree Decision tree is a data mining technique that can be used for classification and prediction of large data. Decision tree is used for profiling customers. Decision tree is also called rule induction technique Luan, It consists of nodes and branches, nodes are connected by branches, time flows from left to right, each ranch represents a decision or a possible event.

Furthermore, simple linear regression and multiple linear regressions are techniques used in regression analysis. These techniques can be done by using SPSS software. Cluster Analysis Customer analysis is an unsupervised learning technique Tsai et al. Customer analysis refers to identifying groups of customers with similar characteristics Ahn and Sohn, , splitting the full data set into a set of clusters Baker, where categories are not known in advance. Han and Kamber indicate that cluster analysis can be used to generate labels.

The objects are clustered or grouped based on the principles of maximizing the intra-class similarly and minimizing the interclass similarity. Clustering is also known as segmentation Sinha et al. Segmentation can be done based on demographic variables, e. Both models are used to predict and classify the large data into usable information.

Consequently, universities can use cluster analysis to examine similarities and differences between colleges, students, teachers, administrative staff, courses, and examinations. They found that students use chat messages to communicate positive emotions, negative emotions, and expressions of social support. In addition, there is no positive correlation between the number of chat messages and final grades. Maqsood states that data mining can be used to report and analyse the data that help in preparing marketing strategies for targeted students.

Samira et al. They suggest that teachers can use data mining to find innovative way to help improving and teaching as well as develop assessment procedures. Cios et al. Some features of the site may not work correctly. DOI: Luan Published 1 March Computer Science New Directions for Institutional Research This chapter examines the theoretical basis for data mining, one of the essential knowledge management processes, and uses a case study to describe its application and impact.

View via Publisher. Save to Library Save. Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures, Tables, and Topics from this paper. Citation Type. View 1 excerpt, cites background. Educational data mining is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data.

The data mining technology can discover the hidden patterns, … Expand. Data mining techniques are playing vital roles in Higher education institution.

This paper is reviewing some … Expand. View 2 excerpts, cites methods and background. Highly Influential. View 5 excerpts, references background. Data Mining: Concepts and Techniques. View 4 excerpts, references background. Data warehousing fundamentals : a comprehensive guide for IT professionals. View 2 excerpts, references background. View 1 excerpt, references methods.

Classification: Classification is a classic data mining technique based on machine learning. Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. A Rule-based classification extracts a set of rules that show relationships between attributes of the data set and the class label.

Association rules are characteristic rules it describes current situation , but classification rules are prediction rules for describing future situation. Clustering: Clustering is a division of data into groups of similar objects. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others[8].

The K-means algorithm, probably the best one of the clustering algorithms proposed, is based on a very simple idea: Given a set of initial clusters, assign each point to one of them, and then each cluster center is replaced by the mean point on the respective cluster. These two simple steps are repeated until convergence [9]. The objective of this k-means test is to choose the best cluster center to be the centric.

The k-means algorithm requires the change of nominal attributes in to numerical. The clustering method produced a model with five clusters. Since the application of data mining brings a lot of advantages in higher learning institution, it is recommended to apply these techniques in the areas like optimization of resources, prediction of retainment of faculties in the university, to find the gap between the number of candidates applied for the post, number of applicants responded, number of applicants appeared, selected and finally joined.

Romero, S.