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....
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Thi-Qar
2019-04-01
|
| Series: | مجلة علوم ذي قار |
| Subjects: | |
| Online Access: | https://mail.jsci.utq.edu.iq/index.php/main/article/view/23 |
| _version_ | 1797454201507282944 |
|---|---|
| author | Naofal Mohamad Hassin Azeez |
| author_facet | Naofal Mohamad Hassin Azeez |
| author_sort | Naofal Mohamad Hassin Azeez |
| collection | DOAJ |
| description | 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.
|
| first_indexed | 2024-03-09T15:33:51Z |
| format | Article |
| id | doaj.art-294ec61900494a68acdd3200d9edb0b2 |
| institution | Directory Open Access Journal |
| issn | 1991-8690 2709-0256 |
| language | English |
| last_indexed | 2024-03-09T15:33:51Z |
| publishDate | 2019-04-01 |
| publisher | University of Thi-Qar |
| record_format | Article |
| series | مجلة علوم ذي قار |
| spelling | doaj.art-294ec61900494a68acdd3200d9edb0b22023-11-26T09:41:44ZengUniversity of Thi-Qarمجلة علوم ذي قار1991-86902709-02562019-04-0162Data Mining Approach for Predicting Learner's AchievementNaofal Mohamad Hassin Azeez0Computer Department/Computer Science and Mathematical CollegeStudent 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. https://mail.jsci.utq.edu.iq/index.php/main/article/view/23Student Achievement VariablesAttribute SelectionDimensionality ReductionRule Extraction, Knowledge Acquisition |
| spellingShingle | Naofal Mohamad Hassin Azeez Data Mining Approach for Predicting Learner's Achievement مجلة علوم ذي قار Student Achievement Variables Attribute Selection Dimensionality Reduction Rule Extraction, Knowledge Acquisition |
| title | Data Mining Approach for Predicting Learner's Achievement |
| title_full | Data Mining Approach for Predicting Learner's Achievement |
| title_fullStr | Data Mining Approach for Predicting Learner's Achievement |
| title_full_unstemmed | Data Mining Approach for Predicting Learner's Achievement |
| title_short | Data Mining Approach for Predicting Learner's Achievement |
| title_sort | data mining approach for predicting learner s achievement |
| topic | Student Achievement Variables Attribute Selection Dimensionality Reduction Rule Extraction, Knowledge Acquisition |
| url | https://mail.jsci.utq.edu.iq/index.php/main/article/view/23 |
| work_keys_str_mv | AT naofalmohamadhassinazeez dataminingapproachforpredictinglearnersachievement |