Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques

2009 ◽  
pp. 12-20 ◽  
Author(s):  
Silvia Baldiris ◽  
Ramón Fabregat ◽  
Carolina Mejía ◽  
Sergio Gómez
Keyword(s):  
Machine Learning ◽  
Learning Management Systems ◽  
Machine Learning Techniques ◽  
Management Systems ◽  
Open Learning ◽  
Learning Techniques ◽  
Learning Management

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.

Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

2011 ◽  
pp. 120-131
Author(s):  
Owen McGrath
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Software ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Open Learning ◽  
User Activity ◽  
Learning Management ◽  
Usage Analysis

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

2010 ◽  
Vol 2 (1) ◽  
pp. 65-75
Author(s):  
Owen McGrath
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Software ◽  
Adaptive Learning ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Open Learning ◽  
User Activity ◽  
Learning Management

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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