scholarly journals A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2677
Author(s):  
Alicia Nieto-Reyes ◽  
Rafael Duque ◽  
Giacomo Francisci
Keyword(s):  
Academic Performance ◽  
Learning Systems ◽  
Software Systems ◽  
Student Records ◽  
Tukey Depth ◽  
Student Academic Performance ◽  
Develop Software ◽  
Using Data ◽  
Academic Year

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.

scholarly journals Monitoring system of student performance using data warehouse (Case study: INSTITUT TEKNOLOGI SUMATERA)

2018 ◽  
Vol 4 ◽  
Author(s):  
Ahmad Luky Ramdani ◽  
Raidah Hanifah ◽  
Okta Pilopa
Keyword(s):  
Student Performance ◽  
Data Warehouse ◽  
Monitoring And Evaluation ◽  
Test Results ◽  
Test Scenario ◽  
Monitoring Process ◽  
Using Data ◽  
Academic Year

Improving the quality of learning is one of the things that must be achieved in the college academic process. To achieve this, monitoring and evaluation of the results of the learning process is needed, namely by looking at student performance. Based on this, the research aims to develop a university data warehouse with student performance objects that will be used by the board application for the monitoring process. The application was successfully developed with several main features, namely: a) displaying the number of students based on year, region and the entrance to college, b) displaying a comparison of the number of students in each academic year based on student status , d) display student performance every academic year and e) KPI values based on needs analysis. These features have been tested using the blackbox approach and the test results show that the features work properly and produce outputs in corresponding to the test scenario.

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

scholarly journals Aspects of Academic Performance and Ethics in the Transition to eLearning Caused by the Actual Pandemic - A case study

TEM Journal ◽  
2020 ◽  
pp. 656-661
Author(s):  
Petre Lucian Ogrutan ◽  
Lia Elena Aciu
Keyword(s):  
Academic Performance ◽  
Academic Integrity ◽  
Information Sources ◽  
First Year ◽  
Distance Teaching ◽  
First Semester ◽  
Teachers And Students ◽  
Academic Year ◽  
School Activity

In the actual context of a great diversity of information sources, the discipline of Ethics and Academic Integrity (EAI) was introduced recently in the first year of the Master's studies. As part of the school activity, the access to anti-plagiarism software was made available to teachers and students. The current pandemic has forced the transition from classical classes to the use of the eLearning platform. In this paper the application of the methods of antiplagiarism verification to the distance teaching and the obtained results are described. The conclusions are expressed by comparing the results obtained for the discipline developed through the eLearning platform with those obtained in the previous years in a face-toface teaching manner and with those of the EAI discipline carried out in the first semester of this academic year as well.

Sign in / Sign up

Export Citation Format

Share Document