Data Mining and Prediction Tools (for Predicting Stndents' Success in Programming Course)

This project addresses the importance of extraction and analysis of data from
different types of educational settings such as computer-based or web-based
educational system (i.e. course management system), classroom environment factors
as well as psychosocial factors in the university, which can affect the students and
use these data to foresee students' learning patterns.
The vast amount of data from different educational settings can be fully utilized to
predict the students' performance or a particular course. However, there is no tool as
such, that can automatically manage, extract and analyze this kind of information.
Besides that, most of the current data mining tools are too complex for educators to
use and their features go well beyond the scope of what an educator might require.
This project will use the data mining approach and techniques in analyzing different
types of data gathered from different educational settings.
The project aims to develop a new data mining and prediction tools, which will
analyze different types of data coming from different educational settings to assist
lecturer to predict students' performance in a programming course.
The scope of study for this project is one of the programming courses m the
university, Advanced Business Application Programming (ABAP) and the
university's E-Learning System.
The main contribution of this project is the development of a new data mining and
analysis tools, that can produce prediction output to assist the lecturer in his or her
decision making activities to improve the learning process in a particular
programming course.