Using data mining to improve student retention in HE: a case study.

Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention
 (National Audition Office, 2007).
Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention

Automatic generation of data merging program codes.

Data merging is an essential part of ETL (Extract-Transform-Load) processes to build a data warehouse system. To avoid rewheeling merging techniques, we propose a Data Merging Meta-model (DMM) and its transformation into executable program codes in the manner of model driven engineering. DMM allows defining relationships of different model entities and their merging types in conceptual level. Our formalized transformation described using ATL (ATLAS Transformation Language) enables automatic generation of PL/SQL packages to execute data merging in commercial ETL tools. With this approach data warehouse engineers can be relieved from the burden of repetitive complex script coding and the pain of maintaining consistency of design and implementation

A case study on model driven data integration for data centric software development.

Model Driven Data Integration is a data integration approach that proactively incorporates and utilizes metadata across the data integration process. By decoupling data and metadata, MDDI drastically reduces complexity of data integration; whilst also providing an integrated standard development method, which is associated with Model Driven Architecture. This paper introduces a case study to adopt MDA technology as an MDDI framework for data centric software development; including data merging and data customization for data mining. A data merging model is also proposed to define relationships between different models at a conceptual level which is then transformed into a physical model. In this case study we collect and integrate historical data from various universities into the Data Warehouse system in order to develop student intervention services through data mining

Exploiting student intervention system using data mining.

With the proliferation of systems that are put for the student use, data related to activities undertaken by the student are on the increasing. However, these vast amounts of data on student and courses are not integrated and could therefore not easily queried or mined. Therefore, relatively little data is turned into knowledge that can be used by the institution learning. In the work presented here, different data sources such as student record system, virtual learning system are integrated and analysed with the intention of linking behaviour pattern to academic histories and other recorded information. These patterns built into data mining models can then be used to predict individual performance with high accuracy. The question addressed in the paper is: how can indicators of problems related to student retention produced by data mining be presented in a way that will be effective. A prototype system that integrates data mining with an intervention system based on game metaphor has been build and piloted in the computing school. Early evaluations of the system have shown that it has been well received at all levels of the institution and by the students