scholarly journals Refining Student Marks based on Enrolled Modules’ Assessment Methods using Data Mining Techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 5205-5210
Author(s):  
M. Alsuwaiket ◽  
A. H. Blasi ◽  
K. Altarawneh
Keyword(s):  
Data Mining ◽  
Data Extraction ◽  
Educational Data Mining ◽  
Assessment Methods ◽  
Preparation Process ◽  
Data Preparation ◽  
Process Data ◽  
Student Records ◽  
Assessment Index ◽  
The Right

Choosing the right and effective way to assess students is one of the most important tasks of higher education. Many studies have shown that students tend to receive higher scores during their studies when assessed by different study methods - which include units that are fully assessed by varying the duration of study or a combination of courses and exams - than by exams alone. Many Educational Data Mining (EDM) studies process data in advance through traditional data extraction, including the data preparation process. In this paper, we propose a different data preparation process by investigating more than 230,000 student records for the preparation of scores. The data have been processed through diverse stages in order to extract a categorical factor through which students’ module marks are refined during the data preparation stage. The results of this work show that students’ final marks should not be isolated from the nature of the enrolled module’s assessment methods. They must rather be investigated thoroughly and considered during EDM’s data pre-processing stage. More generally, educational data should not be prepared in the same way normal data are due to the differences in data sources, applications, and error types. The effect of Module Assessment Index (MAI) on the prediction process using Random Forest and Naive Bayes classification techniques were investigated. It was shown that considering MAI as attribute increases the accuracy of predicting students’ second year averages based on their first-year averages.

scholarly journals Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education

2019 ◽  
Vol 9 (3) ◽  
pp. 4287-4291 ◽  
Author(s):  
M. Alsuwaiket ◽  
A. H. Blasi ◽  
R. A. Al-Msie'deen
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Assessment ◽  
Educational Data Mining ◽  
Assessment Method ◽  
Assessment Methods ◽  
Preparation Process ◽  
Data Preparation ◽  
Data Types ◽  
Process Data

The choice of an effective student assessment method is an issue of interest in Higher Education. Various studies [1] have shown that students tend to get higher marks when assessed through coursework-based assessment methods which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of educational data mining (EDM) studies that pre-process data through conventional data mining processes including data preparation process, but they are using transcript data as they stand without looking at examination and coursework results weighting which could affect prediction accuracy. This paper proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students’ marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students’ module marks are refined during the data preparation process. The results of this work show that students’ final marks should not be isolated from the nature of the enrolled module’s assessment methods. They must rather be investigated thoroughly and considered during EDM’s data pre-processing phases. More generally, it is concluded that educational data should not be prepared in the same way as other data types due to differences as data sources, applications, and types of errors in them. Therefore, an attribute, coursework assessment ratio (CAR), is proposed to be used in order to take the different modules’ assessment methods into account while preparing student transcript data. The effect of CAR on prediction process using the random forest classification technique has been investigated. It is shown that considering CAR as an attribute increases the accuracy of predicting students’ second-year averages based on their first-year results.

scholarly journals An Effectual Ga Based Association Rule Generation and Fuzzy Svm Classification Algorithm for Predicting Students Performance

2019 ◽  
Vol 8 (6) ◽  
pp. 2915-2920
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Association Rule ◽  
Educational Data Mining ◽  
Rule Generation ◽  
Academic Status ◽  
Student Records ◽  
Performance Simulation ◽  
Svm Classification ◽  
Student’S Performance

This investigation provides outcome of utilizing educational data mining [EDM] to design academic performance of students from real time and online dataset collected from colleges. Data mining is determined to examine non-academic and academic data; this model utilizes a classification approach termed as Fuzzy SVM classification with Genetic algorithm to attain effectual understanding of association rule in enrolment and to evaluate data quality for classification, which is identified as prediction task of performance and academic status based on low academic performance. This model attempts to predict student’s performance in grading system. Academic and student records attained from process were considered to train models estimated using cross-validation and formerly records from complete academic performance. Simulation was performed in MATLAB environment and show that academic status prediction is enhanced while hybrid dataset are added. The accuracy was compared with the existing models and shows better trade off than those methods.

scholarly journals A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

2017 ◽  
Vol 5 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Preet Kamal ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
High Probability ◽  
Research Work ◽  
Educational Data Mining ◽  
Dropout Rate ◽  
Educational Background ◽  
Drop Out ◽  
Teaching Learning ◽  
The Right

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction.

scholarly journals Predictive Decision Support System using Logistic Regression and Decision Tree Model Combination for Student Graduation Success Determination

2015 ◽  
Vol 2 ◽  
pp. 144-153
Author(s):  
Ace C. Lagman
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Decision Tree ◽  
Educational Data Mining ◽  
The Philippines ◽  
Full Time ◽  
Model Combination ◽  
Process Data ◽  
Data Set ◽  
Student Graduation

More recently, researchers and higher education institutions are also beginning to explore the potential of data mining in analyzing academic data. The goal of such an endeavor is to find means to improve the services that these institutions provide and to enhance instruction. This type of data mining application is more popularly known as educational data mining or EDM. At present, EDM is more particularly focused on developing tools that can be used to discover patterns in academic data. It is more concerned about exploring a huge amount of data in order to identify patterns about the microconcepts involved in learning. This area of EDM is often referred to as Learning Analytics – at least as it is commonly compared to more prominent data mining approaches that process data from large repository for better decision-making. One main topic under educational data mining is student graduation. In the Philippines According to the National Statistics Office, there is an imbalance between student enrolment and student graduation. Almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate on time. This scenario indicates the need to conduct research in this area in order to build models that can help improve the situation. The study focused to extract hidden patterns from the data set using logistic regression and decision tree algorithms that can be used to predict too early identification of students who are vulnerable to not having graduation on time so proper retention policies and measures be implemented by the administration.

scholarly journals INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ali Akhtar ◽  
Mohammad Serajuddin ◽  
Hasan Zafrul
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Style ◽  
Data Extraction ◽  
Verbal Learning ◽  
Educational Data Mining ◽  
Knowledge Presentation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0782/a.php" alt="Hit counter" /></p>

scholarly journals Efficient Pattern Generation using Declat Algorithm Based on Association Rule Mining in Educational Data Mining

2020 ◽  
Vol 9 (3) ◽  
pp. 896-902
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Extraction ◽  
Educational Data Mining ◽  
Major Effect ◽  
Pattern Generation ◽  
Quality Education ◽  
Rule Mining ◽  
Academic Record

A new application called DM Educational Data Mining (EDM) involves data extraction and analysis from the classroom or area of education. In order for educators to deliver quality education to students, EDM integrates various educational information into its review. The EDM works by translating raw data from education systems invaluable information which could have a major effect on the study of education. The output of each student is measured from the database and must be sufficiently accurate to withstand changes in the academic record. Then we have transformed the overall arrangement into a modified relation for the adequacy of the Declat algorithm. The purpose of this work is to examine how prior researchers, as well as recent data mining trends in educational research, have dealt with data mining. In this paper, collected data comprised of 200 students. We define academic performance & impact of additional issues on the basis of these course’s last grades, indications of attendance, class tests, and term last answer substance add up to marks and so on. Here, we compare the FP-Growth and Eclat with Declat algorithm on the bases of confidence and support value in a relation of execution time & no. of patterns generated. This paper uses a declat algorithm to create patterns or delete effective patterns. Such patterns help to illustrate a growing student's success.

An Insight into Educational Data Mining

2019 ◽  
Vol 7 (2) ◽  
pp. 83-90
Author(s):  
Balwinder Kaur ◽  
Anu Gupta ◽  
R.K.Singla .
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Insight Into

IMPLEMENTASI ALGORITMA K-Modes UNTUK MENENTUKAN STRATEGI MARKETING STMIK BUDI DARMA

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ewin Karman Nduru ◽  
Efori Buulolo ◽  
Pristiwanto Pristiwanto
Keyword(s):  
High School ◽  
Data Mining ◽  
High School Students ◽  
Marketing Strategy ◽  
Process Data ◽  
School Students ◽  
The North ◽  
The Government ◽  
Using Data ◽  
North Sumatra

Universities or institutions that operate in North Sumatra are very many, therefore, of course, competition in accepting new students is very tight, universities or institutions do certain ways or steps to be able to compete with other campuses in gaining interest from community or high school students who will continue their studies to a higher level. STMIK BUDI DARMA Medan (College of Information and Computer Management), is the first computer high school in Medan which was established on March 1, 1996 and received approval from the government through the Minister of Education and Culture, on July 23, 1996 with operating license number 48 / D / O / 1996, in promoting the campus, the team usually formed a promotion team to various regions in the North Sumatra Region to provide information to the community. Students who have learned in this campus are quite a lot who come from various regions in North Sumatra, from this point the need to process data from students who are active in college to be processed using data mining to achieve a target, one method that can be used in data mining, namely the ¬K-Modes clustering (grouping) algorithm. This method is a grouping of student data that will be a help to campus students in promoting, using the K-Modes algorithm is expected to help and become a reference for marketing in determining the marketing strategy STMIK Budi Darma MedanKeywords: STMIK Budi Darma, Marketing Strategy, K-Modes Algorithm.

Sign in / Sign up

Export Citation Format

Share Document