Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.

Data Mining Approach for Predicting Learner's Achievement

Student achievement variables that may be included into student database can be classified into three main categories, student variables. Instructor variables and general variables. This paper presents a new machine-learning model for extracting knowledge From student attributes in a given database. This knowledge can be used for determining the relative importance and effectiveness of student's attributes for the prediction of their college academic achievement, and the relationship between these attributes and their achievement. The model includes three main algorithms namely: preprocessing of database, attribute selection and rule extraction algorithm. Preprocessing of database aims to alleviate the dimensionality of the given database. It is performed according to (i) Detecting memo attributes and abstracting their field values into minimum abstraction level, (ii) Detecting the attributes, which have repeated values (including sparse values), and dropping them from database and (iii) Using fuzzification for transferring the attributes of continuous values into linguistic terms. This transformation leads to reducing the search space. Attribute selection algorithm selects the most relevant attributes set by the calculations of an evaluation function. The resulted set of attributes is passed to rule extraction algorithm for extracting an accurate and comprehensible set of rules.

Finding patterns in student and medical office data using rough sets

Data have been obtained from King Khaled General Hospital in Saudi Arabia. In this project, I am trying to discover patterns in these data by using implemented algorithms in an experimental tool, called Rough Set Graphic User Interface (RSGUI). Several algorithms are available in RSGUI, each of which is based in Rough Set theory. My objective is to find short meaningful predictive rules. First, we need to find a minimum set of attributes that fully characterize the data. Some of the rules generated from this minimum set will be obvious, and therefore uninteresting. Others will be surprising, and therefore interesting. Usual measures of strength of a rule, such as length of the rule, certainty and coverage were considered. In addition, a measure of interestingness of the rules has been developed based on questionnaires administered to human subjects. There were bugs in the RSGUI java codes and one algorithm in particular, Inductive Learning Algorithm (ILA) missed some cases that were subsequently resolved in ILA2 but not updated in RSGUI. I solved the ILA issue on RSGUI. So now ILA on RSGUI is running well and gives good results for all cases encountered in the hospital administration and student records data.Master's These