Predicting student performance in higher education using multi-regression models

Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student’ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, learning management system (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%

Predicting student performance in higher education using multi-regression models

Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student� grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, Learning Management System (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms

Application of Data Mining Techniques for Improving Continuous Integration

Continuous integration is a software development process where members of a team frequently integrate the work done by them. Generally each person integrates at least daily - leading to multiple integrations per day. Integration done by each developer is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach reduces integration problems and allows a team to develop cohesive software rapidly. Continuous Integration doesn’t remove bugs, but it does make them dramatically easier to find and remove. This paper provides an overview of various issues regarding Continuous Integration and how various data mining techniques can be applied in continuous integration data for extracting useful knowledge and solving continuous integration problems

DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system

STUDENT PERFORMANCE ANALYSIS USING C4.5 ALGORITHM TO OPTIMIZE SELECTION

Education is one of the fields that generate heaps of data. Pile of data that can utilized by higher education institutions to improve tertiary performance. One way to process data piles in the education is to use data mining or called education data mining. The quality assessment of educational institutions conducted by the community and the government is strongly influenced by student performance. Students who have poor performance will have a negative impact on educational institutions. Student data is processed to obtain valuable knowledge regarding the classification of student performance. One method of data mining is the C4.5 algorithm which is known to be able to produce good classifications. In this research and optimization method will be used namely optimize selection on the c4.5 algorithm. Based on the research, it is known that the optimization selection optimization method can improve the performance of algorithm c4.5 from 85% to 87%

Diagnosis of Stroke and Diabetes Mellitus With Classification Techniques Using Decision Tree Method

: Stroke is a cerebral vascular disease characterized by the death of brain tissue that occurs due to reduced blood and oxygen flow to the brain. Ischemic stroke is associated with diabetes mellitus, therefore it is important to identify the risk factors that cause stroke and DM by diagnostic cause of the disease. This study aimed to classify and compare accuracy tests on medical record data sets for stroke and DM. This study analyzed the diagnosis of stroke and DM using Decision Tree. The risk factors consisted of gender, age, blood pressure, nutritional status, smoking, history of DM, and history of hypertension. The results of the analysis using the Decision Tree method showed that the accuracy rate was 86.67%, which means that the modeling has a good level of correctness of the prediction results. We conclude that the Decision Tree method was an accurate method for detecting stroke and DM

Prediction of a Sprint Deliverys Capabilities in Iterative-based Software Development

Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This researchs purpose is to give support for a software projects supervisor or owner in predicting the end products capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects

Prediction of a Sprint Deliverys Capabilities in Iterative-based Software Development

Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This researchs purpose is to give support for a software projects supervisor or owner in predicting the end products capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects

Implementing a Students’ Survey System in Iraqi Universities: A Case Study in Basra University

This research deals with total quality management as a method that has been described as the third revolution after the industrial and technological revolutions. The present case study was undertaken to implement a survey system to provide a predefined survey by the Iraqi universities to establish and test dimensions for measuring service quality in higher education. The main purpose of this work is to deploy students’ surveys related to academic subjects to evaluate students’ satisfaction with services provided by Higher Educational Institutions. Specifically, the study found a significant relationship between the five dimensions of service quality (tangibility, reliability, responsiveness, assurance, and students’ satisfaction). The findings generally indicate that the majority of students are satisfied with the proposed survey system. Such findings help universities make a better strategic plan to enhance students’ satisfaction in particular and its overall performance in general