scholarly journals Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2078
Author(s):  
Farrukh Saleem ◽  
Zahid Ullah ◽  
Bahjat Fakieh ◽  
Faris Kateb
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Learning Management Systems ◽  
Machine Learning Algorithms ◽  
Learning Performance ◽  
Management Systems ◽  
Electronic Learning ◽  
Ensemble Techniques ◽  
Proposed Model ◽  
Learning Management

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

scholarly journals The Effects of Using a Fully Integrated Mobile Application to Access Learning Management Systems in Higher Education

2020 ◽  
Vol 26 (9) ◽  
pp. 1213-1229
Author(s):  
José Martín-Núñez ◽  
Susana Sastre ◽  
José Peiró ◽  
José Hilera
Keyword(s):  
Student Satisfaction ◽  
Student Performance ◽  
Mobile Application ◽  
Learning Experience ◽  
Learning Management Systems ◽  
Academic Major ◽  
Management Systems ◽  
Fully Integrated ◽  
Learning Management ◽  
The Web

The use of mobile devices in the classroom is increasingly frequent. However, the LMS are still not completely adapted to this format, preventing students from using all the LMS web-functionalities in their mobiles. Hence, we present and evaluate the use of a new mobile application fully integrated with Learning Management Systems (LMS). We examined access to LMS by 95 postgraduate university students, differentiating between the services accessed and the means used. Students belonged to four consecutive promotions. In the first two, access to the system was through the web, while in the third and fourth, an app fully integrated with the LMS was available. The results showed an overall increase in access to LMS, with a considerable reduction in access via the web in favor of access via the application. Significant differences were found in the access patterns to communication and assessment services depending on the students' age, gender, academic major and previous m-learning experience. Satisfaction with the LMS rose when the app was available, with greater growth within the academic major on IT and previous m-learning experience group. Finally, students with high performance accessed the system significantly more than those with low performance. In conclusion, the integration of the app with the system showed useful and efficient results. The app eased the use of the system, increased student satisfaction with LMS, and student performance improved with increased access.

scholarly journals An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 728 ◽  
Author(s):  
Lijuan Yan ◽  
Yanshen Liu
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Ensemble Prediction ◽  
Support Vector ◽  
Proposed Model ◽  
Importance Analysis ◽  
Better Than

Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area   under   the   receiver   operating   characteristic   curve ( AUC ) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.

scholarly journals Learning Management Systems: A Shift Toward Learning and Academic Analytics

2016 ◽  
Vol 11 (04) ◽  
pp. 77 ◽  
Author(s):  
Adam Marks ◽  
Maytha AL-Ali ◽  
Kees Rietsema
Keyword(s):  
Online Survey ◽  
Learning Management Systems ◽  
Learning Performance ◽  
Management Systems ◽  
Degree Programs ◽  
College Level ◽  
Learning Management ◽  
The University

This paper presents the major findings from a study conducted with six different universities in the U.S. regarding their use of the learning analytics (LA) capabilities available within their learning management systems (LMS). Data was collected from an online survey instrument, in-depth interviews with IT directors and academic administrators, and a case study in Embry-Riddle Aeronautical University. One observation is that universities are attempting to make better use of new analytics functions and the data stored in the university LMS in order to make more informed decisions regarding short-term and long-term goals and objectives. The new functions include analytics performed at the institutional level, college level, degree-program level, course level, and even course section level. Courses and degree programs as well as learning performance and objectives can be measured and analyzed using different goals, criteria, and accreditation requirements.

Using Learning Management System Activity Data to Predict Student Performance in Face-to-Face Courses

2020 ◽  
Vol 12 (3) ◽  
pp. 20-31 ◽  
Author(s):  
Najib Ali Mozahem
Keyword(s):  
Student Performance ◽  
Event History Analysis ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Private University ◽  
Management Systems ◽  
Activity Data ◽  
Face To Face ◽  
History Analysis ◽  
Learning Management

Higher education institutes are increasingly turning their attention to web-based learning management systems. The purpose of this study is to investigate whether data collected from LMS can be used to predict student performance in classrooms that use LMS to supplement face-to-face teaching. Data was collected from eight courses spread across two semesters at a private university in Lebanon. Event history analysis was used to investigate whether the probability of logging in was related to the gender and grade of the students. Results indicate that students with higher grades login more frequently to the LMS, that females login more frequently than males, and that student login activity increases as the semester progresses. As a result, this study shows that login activity can be used to predict the academic performance of students. These findings suggest that educators in traditional face-to-face classes can benefit from educational data mining techniques that are applied to the data collected by learning management systems in order to monitor student performance.

scholarly journals Factors That Affect User Satisfaction Of Electronic Learning Management Systems (ELMSs)

2020 ◽  
Vol 005 (01) ◽  
pp. 8-14
Author(s):  
Riyadi Riyadi ◽  
Umar Nimran ◽  
Eko Ganis Sukoharsono ◽  
Muhammad Al Musadieq
Keyword(s):  
User Satisfaction ◽  
Learning Management Systems ◽  
Management Systems ◽  
Electronic Learning ◽  
Learning Management

scholarly journals Improvement of Student Attention Monitoring Supported by Precision Sensing in Learning Management Systems

2021 ◽  
Author(s):  
Andreia Filipa Valada Pereira Artífice ◽  
João Sarraipa ◽  
Ricardo Jardim-Goncalves
Keyword(s):  
Student Performance ◽  
Communication Systems ◽  
Learning Management Systems ◽  
Management Systems ◽  
Student Centered ◽  
Computer Mediated ◽  
E Learning ◽  
Learning Management ◽  
Automatic Attention ◽  
Student Attention

A Learning Management Systems (LMS) can benefit from the inclusion Computer-Mediated-Communications (CMC) software for delivering materials. Incorporating CMC tools in virtual classrooms or implementing educational blogs, can be very effective in e-learning platforms. In such student-centered interaction scenarios, it is important to monitor and manage student attention in a precise way to enhance student performance. Sensing with precision through 6G/7G technology allows to include electronic and software devices to produce such monitoring. This chapter contextualizes and describes an abstraction application scenario of sensing and monitoring student attention with high precision in Learning Management System with new communication systems. In that context, technology (e.g. sensors), is used to perform automatic attention monitoring, helping to manage students in e-Learning. Additionally, the document presents a possible scenario which supports intelligent services to the monitoring of student attention during e-learning activities in the context of Smart HEI (Higher Education Institutes).

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